CA3166556A1 - Method and device for generating target advertorial based on deep learning - Google Patents
Method and device for generating target advertorial based on deep learningInfo
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Abstract
A deep learning-based target advertorial generating method and apparatus. The method comprises: receiving related information of a target object, matching several adapted target headlines from a headline library according to the related information, the headlines in the headline library being expanded from acquired headlines by means of a third generation model (S1); inputting the target headline into the first generation model to generate at least one target introduction (S2); generating at least one piece of input information that conforms to a preset structure according to the related information and a preset rule, and inputting the input information into a second generation model to generate at least one target body (S3); and assembling the target headline, the target introduction, and the target body to obtain multiple target advertorials (S4). By using deep learning and natural language processing technology, the automatic, intelligent and diversified generation of marketing advertorials can be implemented, the investment of operators is reduced, the production efficiency of marketing advertorials is improved, the problem of low handwriting efficiency is effectively avoided, and the problem of dull template generation is also avoided.
Description
METHOD AND DEVICE FOR GENERATING TARGET ADVERTORIAL BASED ON
DEEP LEARNING
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of natural language processing technology, and more particularly to a method of and a device for generating a target advertorial based on deep learning.
Description of Related Art
DEEP LEARNING
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of natural language processing technology, and more particularly to a method of and a device for generating a target advertorial based on deep learning.
Description of Related Art
[0002] Marketing advertorials are frequently used in the promotion of new products to the market, and a marketing advertorial usually consists of three sections, namely a title, an introduction, and a marketing text. Vivid and concise language is employed in the title to describe the marketed product as attractive to the people, the introduction exerts an introducing function to guide the direction of consumption and lead to the following marketing text, while the marketing text describes the product and recommends purchase thereof.
[0003] At present, marketing advertorials are mostly written manually by operating personnel of the merchants or automatically generated by templates regardless of titles, introductions, and marketing texts. However, both of these methods are more or less defective.
[0004] As regards manual writing, it is required for related personnel to organize vivid language to manually write marketing advertorials according to the categories to be marketed, once it is needed to output great quantities of advertorials or to expand to relatively many categories in a short time, it is usually problematic in terms of low production efficiency.
[0005] As regards template generation, although batch generation is possible in a short time, the statements as generated are problematic in terms of fixed patterns, stereotyped styles, and insufficient diversities, etc.
SUMMARY OF THE INVENTION
SUMMARY OF THE INVENTION
[0006] In order to deal with problems pending in the state of the art, embodiments of the present Date Regue/Date Received 2022-06-29 invention provide a method of and a device for generating a target advertorial based on deep learning, so as to solve such prior-art problems as low production efficiency in manual writing of target advertorials, and fixed patterns, stereotyped styles, and insufficient diversities of statements in template generation of target advertorials.
[0007] In order to solve one or more technical problems mentioned above, the present invention employs the following technical solutions.
[0008] According to one aspect, there is provided a method of generating a target advertorial based on deep learning, the method comprises the following steps:
[0009] receiving relevant information of a target object, and matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model;
[0010] inputting the target titles in a first generating model, and generating at least one target introduction;
[0011] generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text; and
[0012] assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials.
[0013] Further, the step of generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text includes:
[0014] subjecting the relevant information to a term-segmenting process, and extracting target segmented terms that satisfy a preset condition from a first term-segmenting result as obtained;
[0015] recombining the target segmented terms, and obtaining at least one piece of input information that conforms to a preset structure; and Date Regue/Date Received 2022-06-29
[0016] inputting the input information in the second generating model, and generating at least one target text.
[0017] Moreover, the method further comprises a process of constructing the title library, including:
[0018] subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result;
[0019] employing a preset first keyword extracting method to extract a first keyword from the first sample titles; and
[0020] inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
[0021] Moreover, the process of constructing the title library further includes:
[0022] intersecting a first keyword set with the second term-segmenting result, and obtaining an input data set; and
[0023] taking data of the input data set as input, taking the target titles as output, and training out the third generating model based on a preset algorithm.
[0024] Moreover, the method further comprises a process of constructing the first generating model, including:
[0025] subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process;
[0026] employing a preset second keyword extracting method to extract a second keyword from the second sample titles;
[0027] intersecting a second keyword set with each term-segmented second sample title, and obtaining a target keyword;
[0028] traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, and obtaining a Date Regue/Date Received 2022-06-29 successfully matched introduction to serve as a new introduction of the current second sample title; and
[0029] taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm.
[0030] According to another aspect, there is provided a device for generating a target advertorial based on deep learning, the device comprises:
[0031] a title matching module, for receiving relevant information of a target object, and matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model;
[0032] an introduction generating module, for inputting the target titles in a first generating model, and generating at least one target introduction;
[0033] a text generating module, for generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text; and
[0034] an information assembling module, for assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials.
[0035] Further, the text generating module includes:
[0036] a first term-segmenting unit, for subjecting the relevant information to a term-segmenting process, and extracting target segmented terms that satisfy a preset condition from a first term-segmenting result as obtained;
[0037] a segmented terms recombining unit, for recombining the target segmented terms, and obtaining at least one piece of input information that conforms to a preset structure; and
[0038] a text generating unit, for inputting the input information in the second generating model, Date Regue/Date Received 2022-06-29 and generating at least one target text.
[0039] Moreover, the device further comprises a first constructing module that includes:
[0040] a second term-segmenting unit, for subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result;
[0041] a first extracting unit, for employing a preset first keyword extracting method to extract a first keyword from the first sample titles; and
[0042] a title generating unit, for inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
[0043] Moreover, the first constructing module further includes:
[0044] a first intersecting unit, for intersecting a first keyword set with the second term-segmenting result, and obtaining an input data set; and
[0045] a first training unit, for taking data of the input data set as input, taking the target titles as output, and training out the third generating model based on a preset algorithm.
[0046] Moreover, the device further comprises a second constructing module that includes:
[0047] a third term-segmenting unit, for subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process;
[0048] a second extracting unit, for employing a preset second keyword extracting method to extract a second keyword from the second sample titles;
[0049] a second intersecting unit, for intersecting a second keyword set with each term-segmented second sample title, and obtaining a target keyword;
[0050] an introduction expanding unit, for traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, and obtaining a successfully matched introduction to serve as a new introduction of the current second sample title; and Date Regue/Date Received 2022-06-29
[0051] a second training unit, for taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm.
[0052] The technical solutions provided by the embodiments of the present invention bring about the following advantageous effects.
[0053] 1. In the method of and device for generating a target advertorial based on deep learning as provided by the embodiments of the present invention, by receiving relevant information of a target object, matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model, inputting the target titles in a first generating model, generating at least one target introduction, generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, generating at least one target text, assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials, by means of the deep learning and natural language processing technologies, it is made possible to realize automatic, intellectualized, and diversified generation of marketing advertorials, save input of operating personnel, enhance production efficiency of marketing advertorials, effectively avoid the problem concerning low efficiency by manual writing, and avoid the problem concerning the stereotyped style of template generation at the same time.
[0054] 2. In the method of and device for generating a target advertorial based on deep learning as provided by the embodiments of the present invention, by subjecting plural collected first sample titles to a term-segmenting process, obtaining a second term-segmenting result, employing a preset first keyword extracting method to extract a first keyword from the first sample titles, inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, currently available and limited titles are used to expand the number of titles in the title library.
[0055] 3. In the method of and device for generating a target advertorial based on deep learning Date Regue/Date Received 2022-06-29 as provided by the embodiments of the present invention, by subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process, employing a preset second keyword extracting method to extract a second keyword from the second sample titles, intersecting a second keyword set with each term-segmented second sample title, obtaining a target keyword, traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, obtaining a successfully matched introduction to serve as a new introduction of the current second sample title, taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm, training data of an introduction generating model is expanded, and such problems as overfitting and inferior generation effects easily causable by insufficient training data are avoided.
BRIEF DESCRIPTION OF THE DRAWINGS
BRIEF DESCRIPTION OF THE DRAWINGS
[0056] In order to describe the technical solutions in the embodiments of the present invention more clearly, accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Apparently, the accompanying drawings introduced below are merely directed to partial embodiments of the present invention, and it is possible for persons ordinarily skilled in the art to acquire other drawings based on these drawings without spending any creative effort in the process.
[0057] Fig. 1 is a flowchart illustrating the method of generating a target advertorial based on deep learning according to an exemplary embodiment;
[0058] Fig. 2 is a flowchart illustrating the step of generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text according to an exemplary embodiment;
[0059] Fig. 3 is a flowchart illustrating the process of constructing a title library according to an Date Regue/Date Received 2022-06-29 exemplary embodiment;
[0060] Fig. 4 is a flowchart illustrating the process of constructing a title library according to another exemplary embodiment;
[0061] Fig. 5 is a flowchart illustrating the process of constructing a first generating model according to an exemplary embodiment; and
[0062] Fig. 6 is a view schematically illustrating the structure of a device for generating a target advertorial based on deep learning according to an exemplary embodiment.
DETAILED DESCRIPTION OF THE INVENTION
DETAILED DESCRIPTION OF THE INVENTION
[0063] In order to make more lucid and clear the objectives, technical solutions, and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and comprehensively described below with reference to accompanying drawings in the embodiments of the present invention. Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present invention. All other embodiments obtainable by persons ordinarily skilled in the art based on the embodiments in the present invention shall all be covered by the protection scope of the present invention.
[0064] The method of generating a target advertorial based on deep learning provided by the present invention firstly retrieves adaptable titles from a title library according to the relevant information of a target object, then sequentially generates an introduction and marketing statements (namely marketing text) according to the matched titles and the relevant information, and finally assembles a target title, the introduction and the marketing text to output plural marketing advertorials. In the embodiments of the present invention, the Seq2Seq algorithm is employed to realize generation of the introduction and the marketing text, thus making it possible to effectively avoid the problem concerning low efficiency by manual writing, and avoid the problem concerning the stereotyped style of template generation at the same time. Seq2Seq is a generation framework made up of an encoder and a decoder, generates output sequence Y
according Date Regue/Date Received 2022-06-29 to input sequence X, and is widely applied in such tasks as translation, text automatic abstraction, and robot automatic Q&A, etc.
according Date Regue/Date Received 2022-06-29 to input sequence X, and is widely applied in such tasks as translation, text automatic abstraction, and robot automatic Q&A, etc.
[0065] Fig. 1 is a flowchart illustrating the method of generating a target advertorial based on deep learning according to an exemplary embodiment, as shown in Fig. 1, the method comprises the following steps.
[0066] Si - receiving relevant information of a target object, and matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model.
[0067] Specifically, the target advertorial generally includes three sections, namely a title, an introduction, and a text. The target advertorial in the embodiments of the present invention includes a marketing advertorial, the marketing advertorial is taken for example to include three sections, namely a title, an introduction, and a text. The relevant information of the target object in the embodiments of the present invention includes the title of the product of the target advertorial to be generated, or descriptive information of the target object of the target advertorial to be generated. Moreover, in the embodiments of the present invention, the received relevant information can be input by a user, and the relevant information input by a user can be the title(s) of one or more product(s) of a certain category. After the relevant information of the target object input by the user has been received, plural target titles adapted to the relevant information are matched out of a title library according to a preset matching method (such as character strings matching after term segmentation, similarities matching, etc.), wherein titles in the title library are derived by expansion of collected titles by a third generating model. As should be noted here, the title matching method is not specifically defined in the embodiments of the present invention, and it is possible for the user to set the method according to specific requirement.
[0068] S2 - inputting the target title in a first generating model, and generating at least one target introduction.
Date Regue/Date Received 2022-06-29
Date Regue/Date Received 2022-06-29
[0069] Specifically, in the embodiments of the present invention, the first generating model is a natural language processing model pretrained by means of a preset algorithm (such as the 5eq25eq algorithm). The input to the model is the aforementioned target title, and the output therefrom is the target introduction corresponding to the target title, wherein the number of target introduction(s) output from the first generating model can be one or more, to which no restriction is made in this context.
[0070] S3 - generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text.
[0071] Specifically, the second generating model is also a natural language processing model pretrained by means of a preset algorithm (such as the 5eq25eq algorithm). In the embodiments of the present invention, in order for the target text output from the second generating model to be diversified, this is realized by expanding the input to the second generating model. Accordingly, before the target text is generated, at least one piece of input information that conforms to a preset structure is firstly generated according to the relevant information and a preset rule, the obtained input information is subsequently input in the second generating model, and at least one target text is generated. The "at least one" means one or more.
[0072] S4 - assembling the target title, the target introduction, and the target text, and obtaining plural target advertorials.
[0073] Specifically, the target title as well as the target introduction and target text obtained through the foregoing step are finally assembled to obtain plural target advertorials for reference and selection by users.
[0074] With reference to Fig. 2, as a preferred mode of execution in the embodiments of the present invention, the step of generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one Date Regue/Date Received 2022-06-29 target text includes the following.
[0075] S101 - subjecting the relevant information to a term-segmenting process, and extracting target segmented terms that satisfy a preset condition from a first term-segmenting result as obtained.
[0076] Specifically, the relevant information is usually mostly of a structure as "modifier +
category word", of which the modifier is a term expressing brand, function, characteristic, and property, etc. In the embodiments of the present invention, input to the second generating model is expanded by the mode of recombining the sequence of the modifiers, so that the target text output from the second generating model can be diversified.
Accordingly, before the target text is generated, the relevant information should be firstly subjected to a term-segmenting process to obtain a first term-segmenting result, and target segmented terms that satisfy a preset condition are subsequently extracted from the first term-segmenting result. Since input to the second generating model is expanded by the mode of recombining the sequence of the modifiers, the target segmented terms that satisfy a preset condition here are segmented terms that pertain to modifiers in the first term-segmenting result.
category word", of which the modifier is a term expressing brand, function, characteristic, and property, etc. In the embodiments of the present invention, input to the second generating model is expanded by the mode of recombining the sequence of the modifiers, so that the target text output from the second generating model can be diversified.
Accordingly, before the target text is generated, the relevant information should be firstly subjected to a term-segmenting process to obtain a first term-segmenting result, and target segmented terms that satisfy a preset condition are subsequently extracted from the first term-segmenting result. Since input to the second generating model is expanded by the mode of recombining the sequence of the modifiers, the target segmented terms that satisfy a preset condition here are segmented terms that pertain to modifiers in the first term-segmenting result.
[0077] S102 - recombining the target segmented terms, and obtaining at least one piece of input information that conforms to a preset structure.
[0078] Specifically, in the embodiments of the present invention, it is possible to preset a recombining mechanism according to practical requirement, such as to recombine the sequence of modifiers after term segmentation. The target segmented terms obtained in the foregoing step are thereafter recombined according to the recombining mechanism to output plural pieces of input information that conform to a preset structure.
By the same token, the preset structure can be a structure as "modifier + category word", whose setup and adjustment can be made by the user according to practical requirement, and no specific restriction is made thereto in this context.
By the same token, the preset structure can be a structure as "modifier + category word", whose setup and adjustment can be made by the user according to practical requirement, and no specific restriction is made thereto in this context.
[0079] S103 - inputting the input information in the second generating model, and generating at Date Regue/Date Received 2022-06-29 least one target text.
[0080] Specifically, the input information obtained through the foregoing step is finally input in the second generating model, and at least one target text is generated.
[0081] Fig. 3 is a flowchart illustrating the process of constructing a title library according to an exemplary embodiment, with reference to Fig. 3, as a preferred mode of execution in the embodiments of the present invention, the process of constructing the title library includes the following.
[0082] S201 - subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result.
[0083] Specifically, in the embodiments of the present invention, after the relevant information of the target object has been received, adaptable target titles are obtained by the mode of matching from the title library according to the relevant information, however, during the process of constructing the title library, the number of titles actually collected is rather limited. To solve such a problem, in the embodiments of the present invention, the number of titles in the title library is increased by the mode of expanding the limited number of collected titles. During specific expansion of the titles, plural collected first sample titles are subjected to a term-segmenting process, and a second term-segmenting result is obtained.
[0084] S202 - employing a preset first keyword extracting method to extract a first keyword from the first sample titles.
[0085] Specifically, a preset first keyword extracting method is then employed to extract a first keyword from the sample titles, wherein it is possible for the user to set the extraction proportion of the first keyword(s) (namely the proportion of the first keyword(s) in the sample titles) according to practical requirement. As should be noted here, in the embodiments of the present invention, the first keyword extracting method is not specifically defined, and the user can set the method according to practical requirement, for instance, by employing a TS-IDF algorithm, etc.
Date Regue/Date Received 2022-06-29
Date Regue/Date Received 2022-06-29
[0086] S203 - inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
[0087] Specifically, the second term-segmenting result and the first keyword obtained in the foregoing steps are taken as input in the third generating model, the resultant outputs (the outputs are new titles) are expanded titles obtained according to the target titles, and these new titles make up the title library provided by the embodiments of the present invention.
As should be noted here, in the embodiments of the present invention, in the third generating module can be employed a beam search decoder, thus making it possible to generate great quantities of titles.
As should be noted here, in the embodiments of the present invention, in the third generating module can be employed a beam search decoder, thus making it possible to generate great quantities of titles.
[0088] Fig. 4 is a flowchart illustrating the process of constructing a title library according to another exemplary embodiment, with reference to Fig. 4, as a preferred mode of execution in the embodiments of the present invention, the process of constructing the title library includes:
[0089] S301 - subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result;
[0090] S302 - employing a preset first keyword extracting method to extract a first keyword from the sample titles;
[0091] S303 - intersecting a first keyword set with the second term-segmenting result, and obtaining an input data set;
[0092] S304 - taking data of the input data set as input, taking the target titles as output, and training out the third generating model based on a preset algorithm; and
[0093] S305 - inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
[0094] Specifically, the third generating model here is likewise a natural language processing Date Regue/Date Received 2022-06-29 model pretrained by means of a preset algorithm (such as the Seq2Seq algorithm). When training data is prepared for the third generating model, it is possible to calculate the intersection of the first keyword set with the second term-segmenting result obtained in the foregoing steps, obtain an input data set, then take data of the input data set as input, and take the target titles as output to train out the third generating model based on a preset algorithm (such as the Seq2Seq algorithm). In addition, the specific implementation processes of steps S301, S302 and S305 can be inferred with reference to the specific implementation processes of the foregoing steps S201 to S203, while no repetition is redundantly made on a one-by-one basis.
[0095] Moreover, the model under different training states (namely different steps or epochs) can be further employed to repeat the foregoing steps, so as to further expand the titles.
Merely with the aid of existing titles (the first sample titles), the method employs a particular extracting mode to construction input and output to train out the third generating model, so as to make it possible to obtain great quantities of titles with flexible sentence patterns in a short time, save manpower cost, and enhance production efficiency.
Merely with the aid of existing titles (the first sample titles), the method employs a particular extracting mode to construction input and output to train out the third generating model, so as to make it possible to obtain great quantities of titles with flexible sentence patterns in a short time, save manpower cost, and enhance production efficiency.
[0096] Fig. 5 is a flowchart illustrating the process of constructing a first generating model according to an exemplary embodiment, with reference to Fig. 5, as a preferred mode of execution in the embodiments of the present invention, the method further comprises a process of constructing the first generating model, which process includes the following.
[0097] S401 - subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process.
[0098] Specifically, in the embodiments of the present invention, a keyword matching method is employed to mine the inherent relation between titles and introductions, and to match and correspond one title to plural introductions, so that training data of the first generating model can be greatly expanded, such problems as overfitting and inferior generation effect caused by insufficient training data are avoided, and the generation effect of the first generating model is effectively enhanced. During specific implementation, certain amounts of title-introduction pairs are firstly collected in advance, that is, plural second Date Regue/Date Received 2022-06-29 sample titles and introductions corresponding to the second sample titles are collected, the second sample titles and introductions corresponding to the second sample titles are thereafter subjected to a term-segmenting process, and their term-segmenting results are respectively obtained.
[0099] S402 - employing a preset second keyword extracting method to extract a second keyword from the second sample titles.
[0100] Specifically, a preset second keyword extracting method is then employed to extract a second keyword from the second sample titles, wherein it is possible for the user to set the extraction proportion of the second keyword(s) (namely the proportion of the second keyword(s) in the sample titles) according to practical requirement. As should be noted here, in the embodiments of the present invention, the second keyword extracting method is also not specifically defined, and the user can set the method according to practical requirement, for instance, by employing a TS-IDF algorithm, etc.
[0101] S403 - intersecting a second keyword set with each term-segmented second sample title, and obtaining a target keyword.
[0102] Specifically, a target keyword is extracted from each second sample title, during specific implementation, it is possible to calculate the intersection of the second keyword set with each term-segmented second sample title, and to take the result obtained by such intersection calculation as a target keyword.
[0103] S404 - traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, and obtaining a successfully matched introduction to serve as a new introduction of the current second sample title.
[0104] Specifically, an optimal matching criterion is preset according to practical requirement, for instance, sorting is effected according to numbers of matched keywords, and top-ten introductions with the maximum numbers of matched keywords are selected to serve as the introductions to which the title corresponds. Each second sample title is traversed, Date Regue/Date Received 2022-06-29 target keywords of each second sample title are used to match in the introductions after total term segmentation, and plural successfully matched introductions are obtained according to the preset optimal matching criterion to serve as new introductions of the current second sample title, whereby the volume of data can be greatly expanded.
[0105] S405 - taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm.
[0106] Specifically, the first generating model is also a natural language processing model pretrained by means of a preset algorithm (such as the 5eq25eq algorithm).
Finally, the second sample title is taken as input, the introduction corresponding to the second sample title and the new introduction expanded in the foregoing step are taken as output, and the first generating model is trained out based on the preset algorithm.
Finally, the second sample title is taken as input, the introduction corresponding to the second sample title and the new introduction expanded in the foregoing step are taken as output, and the first generating model is trained out based on the preset algorithm.
[0107] Fig. 6 is a view schematically illustrating the structure of a device for generating a target advertorial based on deep learning according to an exemplary embodiment, with reference to Fig. 6, the device comprises:
[0108] a title matching module, for receiving relevant information of a target object, and matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model;
[0109] an introduction generating module, for inputting the target titles in a first generating model, and generating at least one target introduction;
[0110] a text generating module, for generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text; and
[0111] an information assembling module, for assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials.
Date Regue/Date Received 2022-06-29
Date Regue/Date Received 2022-06-29
[0112] As a preferred mode of execution in the embodiments of the present invention, the text generating module includes:
[0113] a first term-segmenting unit, for subjecting the relevant information to a term-segmenting process, and extracting target segmented terms that satisfy a preset condition from a first term-segmenting result as obtained;
[0114] a segmented terms recombining unit, for recombining the target segmented terms, and obtaining at least one piece of input information that conforms to a preset structure; and
[0115] a text generating unit, for inputting the input information in the second generating model, and generating at least one target text.
[0116] As a preferred mode of execution in the embodiments of the present invention, the device further comprises a first constructing module that includes:
[0117] a second term-segmenting unit, for subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result;
[0118] a first extracting unit, for employing a preset first keyword extracting method to extract a first keyword from the first sample titles; and
[0119] a title generating unit, for inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
[0120] As a preferred mode of execution in the embodiments of the present invention, the first constructing module further includes:
[0121] a first intersecting unit, for intersecting a first keyword set with the second term-segmenting result, and obtaining an input data set; and
[0122] a first training unit, for taking data of the input data set as input, taking the target titles as output, and training out the third generating model based on a preset algorithm.
[0123] As a preferred mode of execution in the embodiments of the present invention, the device Date Regue/Date Received 2022-06-29 further comprises a second constructing module that includes:
[0124] a third term-segmenting unit, for subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process;
[0125] a second extracting unit, for employing a preset second keyword extracting method to extract a second keyword from the second sample titles;
[0126] a second intersecting unit, for intersecting a second keyword set with each term-segmented second sample title, and obtaining a target keyword;
[0127] an introduction expanding unit, for traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, and obtaining a successfully matched introduction to serve as a new introduction of the current second sample title; and
[0128] a second training unit, for taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm.
[0129] In summary, the technical solutions provided by the embodiments of the present invention bring about the following advantageous effects.
[0130] 1. In the method of and device for generating a target advertorial based on deep learning as provided by the embodiments of the present invention, by receiving relevant information of a target object, matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model, inputting the target titles in a first generating model, generating at least one target introduction, generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, generating at least one target text, assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials, by means of the deep learning and natural language processing technologies, it is made possible to realize Date Regue/Date Received 2022-06-29 automatic, intellectualized, and diversified generation of marketing advertorials, save input of operating personnel, enhance production efficiency of marketing advertorials, effectively avoid the problem concerning low efficiency by manual writing, and avoid the problem concerning the stereotyped style of template generation at the same time.
[0131] 2. In the method of and device for generating a target advertorial based on deep learning as provided by the embodiments of the present invention, by subjecting plural collected first sample titles to a term-segmenting process, obtaining a second term-segmenting result, employing a preset first keyword extracting method to extract a first keyword from the first sample titles, inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, currently available and limited titles are used to expand the number of titles in the title library.
[0132] 3. In the method of and device for generating a target advertorial based on deep learning as provided by the embodiments of the present invention, by subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process, employing a preset second keyword extracting method to extract a second keyword from the second sample titles, intersecting a second keyword set with each term-segmented second sample title, obtaining a target keyword, traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, obtaining a successfully matched introduction to serve as a new introduction of the current second sample title, taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm, training data of an introduction generating model is expanded, and such problems as overfitting and inferior generation effects easily causable by insufficient training data are avoided.
[0133] As should be noted, when the device for generating a target advertorial based on deep learning as provided by the foregoing embodiment triggers a target advertorial generating business, it is merely exemplarily described by being divided into the aforementioned Date Regue/Date Received 2022-06-29 various functional modules, whereas in practical application, it is possible to assign these functions to different functional modules to be completed there according to requirements, that is to say, the internal structure of the device can be divided into different functional modules to complete the entire or partial functions mentioned above. In addition, the device for generating a target advertorial based on deep learning as provided by the foregoing embodiment pertains to the same conception as the method of generating a target advertorial based on deep learning as provided by the method embodiment, that is to say, the device is based on the method of generating a target advertorial based on deep learning ¨ see the method embodiments for details of its specific implementation process, while no repetition is made in this context.
[0134] As can be understood by persons ordinarily skilled in the art, the entire or partial steps that realize the aforementioned embodiments can be completed via hardware, and can also be completed via a program that instructs relevant hardware, the program can be stored in a computer-readable storage medium, and the storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.
[0135] What the above describes is merely directed to preferred embodiments of the present invention, and is not meant to restrict the present invention. Any modification, equivalent substitution, and improvement makeable within the spirit and principle of the present invention shall all be covered by the protection scope of the present invention.
Date Regue/Date Received 2022-06-29
Date Regue/Date Received 2022-06-29
Claims (10)
1. A method of generating a target advertorial based on deep learning, characterized in that the method comprises the following steps:
receiving relevant information of a target object, and matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model;
inputting the target titles in a first generating model, and generating at least one target introducti on;
generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text; and assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials.
receiving relevant information of a target object, and matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model;
inputting the target titles in a first generating model, and generating at least one target introducti on;
generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text; and assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials.
2. The method of generating a target advertorial based on deep learning according to Claim 1, characterized in that the step of generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text includes:
subjecting the relevant information to a term-segmenting process, and extracting target segmented terms that satisfy a preset condition from a first term-segmenting result as obtained;
recombining the target segmented terms, and obtaining at least one piece of input information that conforms to a preset structure; and inputting the input information in the second generating model, and generating at least one target text.
subjecting the relevant information to a term-segmenting process, and extracting target segmented terms that satisfy a preset condition from a first term-segmenting result as obtained;
recombining the target segmented terms, and obtaining at least one piece of input information that conforms to a preset structure; and inputting the input information in the second generating model, and generating at least one target text.
3. The method of generating a target advertorial based on deep learning according to Claim 1 or Date Regue/Date Received 2022-06-29 2, characterized in that the method further comprises a process of constructing the title library, including:
subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result;
employing a preset first keyword extracting method to extract a first keyword from the first sample titles; and inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result;
employing a preset first keyword extracting method to extract a first keyword from the first sample titles; and inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
4. The method of generating a target advertorial based on deep learning according to Claim 3, characterized in that the process of constructing the title library further includes:
intersecting a first keyword set with the second term-segmenting result, and obtaining an input data set; and taking data of the input data set as input, taking the target titles as output, and training out the third generating model based on a preset algorithm.
intersecting a first keyword set with the second term-segmenting result, and obtaining an input data set; and taking data of the input data set as input, taking the target titles as output, and training out the third generating model based on a preset algorithm.
5. The method of generating a target advertorial based on deep learning according to Claim 1 or 2, characterized in that the method further comprises a process of constructing the first generating model, including:
subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process;
employing a preset second keyword extracting method to extract a second keyword from the second sample titles;
intersecting a second keyword set with each term-segmented second sample title, and obtaining a target keyword;
traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, and obtaining a successfully Date Regue/Date Received 2022-06-29 matched introduction to serve as a new introduction of the current second sample title; and taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm.
subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process;
employing a preset second keyword extracting method to extract a second keyword from the second sample titles;
intersecting a second keyword set with each term-segmented second sample title, and obtaining a target keyword;
traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, and obtaining a successfully Date Regue/Date Received 2022-06-29 matched introduction to serve as a new introduction of the current second sample title; and taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm.
6. A device for generating a target advertorial based on deep learning, characterized in that the device comprises:
a title matching module, for receiving relevant information of a target object, and matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model;
an introduction generating module, for inputting the target titles in a first generating model, and generating at least one target introduction;
a text generating module, for generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text; and an information assembling module, for assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials.
a title matching module, for receiving relevant information of a target object, and matching plural adaptable target titles out of a title library according to the relevant information, wherein titles in the title library are derived by expansion of collected titles by a third generating model;
an introduction generating module, for inputting the target titles in a first generating model, and generating at least one target introduction;
a text generating module, for generating at least one piece of input information that conforms to a preset structure according to the relevant information and a preset rule, inputting the input information in a second generating model, and generating at least one target text; and an information assembling module, for assembling the target titles, the target introduction, and the target text, and obtaining plural target advertorials.
7. The device for generating a target advertorial based on deep learning according to Claim 6, characterized in that the text generating module includes:
a first term-segmenting unit, for subjecting the relevant information to a term-segmenting process, and extracting target segmented terms that satisfy a preset condition from a first term-segmenting result as obtained;
a segmented terms recombining unit, for recombining the target segmented terms, and obtaining at least one piece of input information that conforms to a preset structure;
and a text generating unit, for inputting the input information in the second generating model, and generating at least one target text.
Date Regue/Date Received 2022-06-29
a first term-segmenting unit, for subjecting the relevant information to a term-segmenting process, and extracting target segmented terms that satisfy a preset condition from a first term-segmenting result as obtained;
a segmented terms recombining unit, for recombining the target segmented terms, and obtaining at least one piece of input information that conforms to a preset structure;
and a text generating unit, for inputting the input information in the second generating model, and generating at least one target text.
Date Regue/Date Received 2022-06-29
8. The device for generating a target advertorial based on deep learning according to Claim 6 or 7, characterized in that the device further comprises a first constructing module that includes:
a second term-segmenting unit, for subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result;
a first extracting unit, for employing a preset first keyword extracting method to extract a first keyword from the first sample titles; and a title generating unit, for inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
a second term-segmenting unit, for subjecting plural collected first sample titles to a term-segmenting process, and obtaining a second term-segmenting result;
a first extracting unit, for employing a preset first keyword extracting method to extract a first keyword from the first sample titles; and a title generating unit, for inputting the second term-segmenting result and the first keyword in the third generating model, and obtaining plural new titles, wherein the title library consists of the new titles.
9. The device for generating a target advertorial based on deep learning according to Claim 8, characterized in that the first constructing module further includes:
a first intersecting unit, for intersecting a first keyword set with the second term-segmenting result, and obtaining an input data set; and a first training unit, for taking data of the input data set as input, taking the target titles as output, and training out the third generating model based on a preset algorithm.
a first intersecting unit, for intersecting a first keyword set with the second term-segmenting result, and obtaining an input data set; and a first training unit, for taking data of the input data set as input, taking the target titles as output, and training out the third generating model based on a preset algorithm.
10. The device for generating a target advertorial based on deep learning according to Claim 6 or 7, characterized in that the device further comprises a second constructing module that includes:
a third term-segmenting unit, for subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process;
a second extracting unit, for employing a preset second keyword extracting method to extract a second keyword from the second sample titles;
a second intersecting unit, for intersecting a second keyword set with each term-segmented second sample title, and obtaining a target keyword;
an introduction expanding unit, for traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, Date Regue/Date Received 2022-06-29 and obtaining a successfully matched introduction to serve as a new introduction of the current second sample title; and a second training unit, for taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm.
Date Regue/Date Received 2022-06-29
a third term-segmenting unit, for subjecting plural collected second sample titles and introductions corresponding to the second sample titles to a term-segmenting process;
a second extracting unit, for employing a preset second keyword extracting method to extract a second keyword from the second sample titles;
a second intersecting unit, for intersecting a second keyword set with each term-segmented second sample title, and obtaining a target keyword;
an introduction expanding unit, for traversing each said second sample title, matching the target keyword with the totally term-segmented introductions corresponding to the second sample titles, Date Regue/Date Received 2022-06-29 and obtaining a successfully matched introduction to serve as a new introduction of the current second sample title; and a second training unit, for taking the second sample title as input, taking the introduction corresponding to the second sample title and the new introduction as output, and training out the first generating model based on a preset algorithm.
Date Regue/Date Received 2022-06-29
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| PCT/CN2020/097007 WO2021135091A1 (en) | 2019-12-30 | 2020-06-19 | Deep learning-based target advertorial generating method and apparatus |
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