WO2019231635A1

WO2019231635A1 - Method and apparatus for generating digest for broadcasting

Info

Publication number: WO2019231635A1
Application number: PCT/US2019/031908
Authority: WO
Inventors: Lei CUI; Shaohan HUANG; Tao GE; Furu Wei; Ming Zhou
Original assignee: Microsoft Technology Licensing, Llc
Priority date: 2018-05-30
Filing date: 2019-05-13
Publication date: 2019-12-05
Also published as: CN110555202A

Abstract

Various embodiments of the present disclosure provide a method of generating a digest broadcast, including: selecting at least one candidate word associated with an event having a predefined spread extent; generating a digest from the candidate word based on a digest template, the digest template at least indicating the following: a word number range of the digest, template elements required for forming the digest and a semantic relation among the template elements; and broadcasting the digest in a speech form.

Description

METHOD AND APPARATUS FOR GENERATING DIGEST FOR

BROADCASTING

FIELD

[0001] The present disclosure generally relates to the information technology, and more particularly, to a method and an apparatus for generating a digest for broadcasting.

BACKGROUND

[0002] Radio stations and other types of content providers normally acquire contents, such as hot-spot events, information and the like, in multiple ways so as to broadcast the same to users. However, currently the contents broadcast by the radio stations or content providers are typically formed by human editors after collecting and processing the current hot events. It is impossible for such broadcast systems which strongly rely on human editors to provide so-called“timely” and“all-day” broadcast service for 7 days and 24 hours per day. Besides, this broadcast system is also unable to provide accurate and customized broadcast service.

SUMMARY

[0003] In order to further improve the user’s experience, various embodiments of the present disclosure provide a digest generating method based on an artificial intelligence (AI) technology. Based on the method, one or more hot lexical terms associated with a hot event or significant event can be acquired automatically. Subsequently, using a digest template and aided by the Artificial Intelligence (AI) technology, a digest can be generated based on these hot lexical terms. Finally, the generated digest is converted into a speech form suitable to be broadcast to a user. As a result, the user can be provided with a timely and accurate broadcast on current hot events or significant social news, without requiring an intervention from the human editors.

[0004] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Fig. 1 illustrates a computer system diagram in which embodiments of the present disclosure can be implemented;

[0006] Fig. 2 illustrates a flowchart of a method of generating a digest for broadcasting according to an embodiment of the present disclosure;

[0007] Fig. 3 illustrates a diagram of an outburst network according to an embodiment of the present disclosure; and

[0008] Fig. 4 illustrates a block diagram of a digest generation module according to an embodiment of the present disclosure.

[0009] Throughout the drawings, the same or similar reference symbols refer to the same or similar elements.

DETAILED DESCRIPTION OF EMBODIMENTS

[0010] The present disclosure will now be described with reference to various example embodiments. It should be appreciated that description of those embodiments is merely to enable those skilled in the art to better understand and further implement the present disclosure and is not intended for limiting the scope disclosed herein in any manner.

[0011] As used herein, the term“includes” and its variants are to be read as open-ended terms that mean“includes, but is not limited to.” The term“based on” is to be read as “based at least in part on.” The term“one example embodiment” and“an example embodiment” are to be read as“at least one example embodiment.” The term“one example embodiment” and“an embodiment” are to be read as“at least one example embodiment.” The term“another embodiment” is to be read as“at least one further embodiment.” The term“first,”“second” or the like can represent different or the same objects. Other definitions, either explicit or implicit, may be included below.

[0012] Basic principles and various example implementations of the present disclosure will now be described with reference to the drawings. Fig. 1 illustrates a block diagram of an apparatus 100 in which multiple implementations of the present disclosure can be implemented. It would be appreciated that the apparatus 100 described in Fig. 1 is merely for illustration and not limit the function and scope of implementations of the present disclosure in any manners. As shown in Fig. 1, the apparatus 100 includes an apparatus in form of a general computing apparatus. Components of the apparatus 100 include, but are not limited to, one or more processors or processing units 110, a memory 120, a storage device 130, one or more communication units 140, one or more input devices 150, and one or more output devices 160.

[0013] In some embodiments, the apparatus 100 may be implemented as various user terminals or server terminals. The server terminal may be a service, large-scale computing scale and the like, which is provided by various service providers. The user terminal may be any type of mobile terminal, fixed terminal or portable terminal, such as mobile telephone, multimedia computer, multimedia tablet, Internet node, communicator, desk-top computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal navigation device, personal digital assistant (PDA), audio/video player, digital camera/video camera, positioning device, TV receiver, radio broadcast receiver, E-book device, gaming device or any combinations thereof, including accessories and peripherals of these devices or any combinations thereof. It would be appreciated that the apparatus 100 can support any type of interface for a user (such as a“wearable” circuit).

[0014] The processing unit 110 can be a physical or virtual processor and can execute various processes based on the programs stored in the memory 120. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capacity of the apparatus 100. The processing unit 110 can also be referred to as central processing unit (CPU), microprocessor, controller or microcontroller.

[0015] The apparatus 100 typically includes a plurality of computer storage media, which can be any available media accessible by the apparatus 100, including but not limited to volatile and non-volatile media, and removable and non-removable media. The memory 120 can be a volatile memory (for example, a register, cache, Random Access Memory (RAM)), non-volatile memory (for example, a Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory), or any combination thereof.

[0016] The memory 120 may include a digest generation module 122 which is configured to execute functionalities of various embodiments as described herein. It is to be noted that the two terms“digest generating method” and“digest generation module” can be used herein exchangeably. The digest generation module 122 is accessible and operable by the processing unit 110 to implement the respective function. The storage device 130 can be any removable or non-removable media and may include machine-readable media, which can be used for storing information and/or data and accessed in the apparatus 100.

[0017] The communication unit 140 communicates with a further computing device via communication media. Additionally, functions of components in the apparatus 100 can be implemented by a single computing cluster or multiple computing machines connected communicatively for communication. Therefore, the apparatus 100 can be operated in a networking environment using a logical link with one or more other servers, network personal computers (PCs) or another general network node. The apparatus 100 can also communicate via the communication unit 140 with one or more external devices (not shown) such as a storage device, display device and the like, one or more devices that enable users to interact with the apparatus 100, or any devices that enable the apparatus 100 to communicate with one or more other computing devices (for example, a network card, modem, and the like). Such communication is performed via an input/output (I/O) interface (not shown).

[0018] The input device 150 may include one or more input devices, such as a mouse, keyboard, tracking ball, voice-input device, and the like. The output device 160 may include one or more output devices, such as a display, loudspeaker, printer, and the like.

[0019] Principles of the embodiments of the present disclosure will be discussed below.

[0020] The apparatus 100 can receive contents actively or passively via the communication unit 140 from various network resources, such as news websites, blogs, self- media and the like. The apparatus 100 can receive contents in response to a user request received via the input device 140. Subsequently, the digest generation module 122 can acquire one or more candidate words from the received contents and generate a new digest based on these candidate words, and then convert the generated digest into a speech form and provide the same to the output device 160 (for example, a loudspeaker), so as to broadcast the formed digest to the users.

[0021] Moreover, the digest generation module 122 can additionally provide the output (for example, a visualized digest list) to the output device 160 (such as a display or the like) for user’s review and selection. For example, the user can selectively play, via the input device 150 (such as a touch screen or the like), the digests of his/her interest from the multiple output digests, or manually skip the digest items that are not of his/her interest. It is to be appreciated that the communication between the digest generation module 122 and the input and output devices 150 and 160 can be implemented via an interface provided by an operating system (OS) on the apparatus 100. Examples of the interface include, but are not limited to, various application program interfaces (APIs).

[0022] The digest generating method as proposed in the embodiments of the present disclosure comprises automatically generating and pushing contents that are possibly of user’s interests based on hot events, top search events, or important events being occurred or occurred during a past period of time. As such, without the need of actively searching for related contents, a user can listen to the related digests based on the objectively significant events or on the hot events acknowledged/accepted by the public. It is to be noted that the digest for broadcasting to the user, as described herein, is not completely equivalent to the form of the traditional news or blogs, but in a more concise form.

[0023] In addition, with the development of artificial intelligence (AI) technologies, an auto-play system (for example, a podcast) can be constructed according to the digest generating method as proposed in the embodiments of the present disclosure. The AI- supporting play system can provide non-intermittent broadcast service for 7 days and 24 hours per day, without requiring the intervention from human editors. In this way, the user’s listening experience can be improved remarkably.

[0024] Fig. 2 illustrates a flowchart of a method 200 of generating a digest for broadcasting according to an embodiment of the present disclosure. It would be appreciated that the method 200 can be implemented by the digest generation module 122 of the apparatus 100.

[0025] At 210, one or more candidate words are selected, which are related to an event having a predefined spread extent. The event having a predefined spread extent comprises an event having certain hotness and/or having certain spread breadth. For example, according to the embodiments of the present disclosure, the candidate words can be acquired from any textual portions of the existing text documents from various network resources (such as news, blogs, self-media and the like). For example, one or more candidate words can be acquired from the title, first paragraph, and body part of an article, or can be acquired from any other parts, such as the bibliography, the header, footer or the like. Of course, in other embodiments, an audio document, video document and/or the textual portions in a picture, for example, can be converted into a text form, allowing extraction of candidate words therefrom. For ease of illustration, embodiments of the present disclosure will be described below with a document as an example.

[0026] In some embodiments, selecting one or more candidate words may comprise: selecting the one or more candidate words from the input query items. For example, the query items input by the user in, for example, a search engine can be used as candidate words.

[0027] In some embodiments, selecting one or more candidate words may further comprise: selecting the one or more candidate words that have been searched (for example, through a search engine) within a predetermined period of time a number times greater than a first predefined threshold (also referred to as“hot search candidate words” herein), or selecting one or more candidate words that have been clicked within a predetermined period of time a number of times greater than a second predefined threshold (also referred to as “hot click candidate words” herein). Such candidate words as described above may be for example the candidate words that have been accumulated searched or clicked by a large number of users during the past hours, days or weeks, a predetermined times greater than a predefined times. [0028] Alternatively, or in addition, selecting one or more candidate words may further comprise: selecting one or more candidate words with frequency of occurrence higher than a third predefined threshold within a predetermined period of time. These candidate words, for example, may be candidate words from one or more documents (also referred to as “outburst documents” herein) associated with occurrence of particular objective events (for example, some events having significant social impacts) within a predetermined period of time. In some embodiments, the outburst documents may be those documents having been previously detected. In some other example embodiments, the outburst documents may require further determination, which will be described below in detail.

[0029] Lexical terms in the outburst documents can be acquired as candidate words. For example, outburst documents may be text flows of news relating to the 7.0 earthquake in Haiti emerging from January 12 to January 31, 2010, and text flows of news relating to the 8.8 earthquake in Chile emerging from February 27 to March 7, 2010. Due to more than 10,000 deaths and immeasurable economic losses caused by the two earthquakes, the lexical term“earthquake” becomes hot spots discussed by the news media since the earthquakes, resulting in a dramatic rise of the word frequency within a certain period of time after the earthquakes, which dramatic rise of the word frequency is thus referred to as“outburst” of the lexical term.

[0030] In some example embodiments, outburst of a certain lexical term can be detected in the following manner. Specifically, an outburst sequence of a lexical term can be defined as

_w ere the value of st is 1 or 0, which respectively represents outburst or not outburst of the lexical term at the time point t. An outburst state of a lexical term can be determined through minimizing the following loss function:

(1)

T is a predefined period of time; p_t is a probability of the lexical term occurring at the time point t in a timer period T; the value of q^(st) is q⁽⁰⁾ or q⁽¹⁾, in which q⁽⁰⁾ is a basic probability of the lexical term, which is typically defined as an average probability of the lexical unit occurring per unit of time in the entire text flow; q⁽¹⁾ is a probability of occurrence at the outburst state of the lexical term, and q⁽¹⁾ is typically defined as:

[0031] The parameter a in the equation (2) can be regarded as an outburst magnification. In other words, a word tends to be considered as being at the outburst state when its word frequency occurring during a certain period of time is close to or beyond a times of its normal word frequency. The value of the parameter a can be adjusted according to the actual task or needs. When determining an outburst state 5 of a lexical term, specifically, the first term at the right part of the equation (1) measures a ratio of p^l to q^(st). If the lexical term outbursts at the time point t (i.e., p^l is much greater than q⁽⁰⁾), i^S Pt’

^cf will be less than

¾ i , and the value of s^l will tend to be 1 in the optimal sequence

S; otherwise, the value of s^l will tend to be 0. The last term at the right part of the equation (1) is provided to smooth (or“punish”) sudden changes or frequent changes of the outburst state in the outburst state sequence, and the parameter b in the equation (1) is the parameter used for controlling weight of this part.

[0032] It is to be noted that, if a certain lexical term is in an outburst state every moment within a certain period of time, this period of time is referred to as an outburst period of time of the lexical term, or in other words, the lexical term outbursts within the period of time. For example, in the example as described above, there are two outburst periods of time for the word“earthquake”, namely“from January 12 to January 31, 2010” and“from February 27 to March 7, 2010”.

[0033] Continuing to refer to Fig. 2, at 220, a digest is generated from one or more candidate words acquired in the above manner, based on a digest template. Herein, the digest template indicates at least the following items: a word number range of the digest, template elements required for forming the digest (for example, essential elements for forming the digest, such as time, a place, persons, a subject, a predicate, an object and so on), and a semantic relationship among the template elements.

[0034] For example, lengthy articles can be first filtered out by using the word number range of the digest, and this is necessary for the news digest generation because the news digest should be concise in most cases. Moreover, the generated digest should conform to a predefined semantic relationship. For example, the semantic relationship may require that some template elements, such as the time of occurrence, place, persons and the like, need to be presented at the head of the digest, and then followed by the description and comments on the specific event. As an another example, the semantic relationship may require that in the digest template the digest must contain elements, such as a subject, predicate, object, attribute, adverbial, complement and the like, and a relative positional relationship among these elements may be further predetermined, so as to enable the digest to conform to the expression rules of the natural language.

[0035] There are multiple digest generating manners. For example, in some embodiments, all or a part of a digest for broadcasting can be generated in an“extracted” manner. Specifically, through candidate words, a document associated therewith can be first determined. In the context, the document associated with the candidate words indicates a document containing one or more candidate words of the candidate words as mentioned above. Subsequently, the existing complete sentence is extracted from the document (note that the sentence may contain or may not contain the candidate word). Finally, the complete sentence extracted based on the above digest template is used directly for a digest to be generated, i.e., the content of the digest is formed by sentences. In the “extracted” digest generation as discussed above, a number of sentences for forming the digest can be limited by the word number range/limitation of the digest template, and the order of the sentences is further determined based on the semantic relationship in the digest template. For example, it may require that the sentence containing a time adverbial and a place adverbial is presented prior to the sentence containing the subject.

[0036] In some embodiments, it can be first determined whether the extracted existing complete sentence contains the foregoing template elements and satisfies the predefined sematic relationship, and only when it is determined that the sentence contains the foregoing template elements and satisfies the predefined semantic relationship, the sentence can be used to generate the digest.

[0037] In some embodiments, a digest can be generated through a“generated” manner. Specifically, the acquired candidate word can be used as the basic unit to directly generate all or a part of a digest broadcast based on the above digest template. It is to be noted that the“generated” manner of digest generation possibly requires adding extra words to assist in forming the digest satisfying a certain semantic relationship.

[0038] In some embodiments for generating a digest based on the“generated” manner, some digests not satisfying the digest template may be removed, after the digest is generated. Alternatively, or in addition, in some embodiments for generating a digest based on the “generated” manner, for example, a“penalty term” can be introduced into the training procedure of the digest generation model, so as to impose a certain penalty on the digest generation model in case that the digest generated in the training procedure does not satisfy the digest template.

[0039] It would be appreciated that a digest can include both the digest portions formed in the“extracted” manner and the digest portion formed in the“generated” manner.

[0040] In some embodiments, in a case that there are a plurality of candidate words, a part of related candidate words can be further selected based on a relevance between any two candidate words. Subsequently, only based on the selected candidate words, the document associated therewith is determined. For example, if the relevance between the two words is relatively low (or the two words have a less important relevance), a document will not be determined based on both of the two candidate words simultaneously. On the contrary, if the relevance between the two candidate words is relatively high (or the two candidate words have an important relevance), it is likely that a document is determined based on both of the two candidate words simultaneously.

[0041] In some embodiments, the relevance between two candidate words can be the number of times for the two candidate words concurrently occurring in the same document. Intuitively, in a text flow, the candidate words concurrently occurring more frequently in the same document probably have a higher relevance. In some embodiments, particularly in a case that there are a large amount of candidate words, a so-called“outburst information network” can be established to assist in determining candidate words and a document associated therewith. The description will be made below with reference to the example of Fig. 3.

[0042] Fig. 3 illustrates a diagram of an example outburst information network 300. As shown therein, the outburst information network 300 is consisted of a plurality of nodes associated with one another (for example, nodes 301, 302, 303, 304, 305 ...), each of which includes a respective lexical term and an outburst period of time of the lexical term. Particularly, each node can be represented as an element group <w, P>, in which“w” represents a lexical term, and“P” represents an outburst period of time of the lexical term “w.” Taking the node 301 as an example, the lexical term“w” included in the node 301 is “Haiti”, and the outburst period of time of the lexical term“Haiti” lasts“from January 12 to January 31.”

[0043] The plurality of nodes can be interconnected based on the relevance among them. For example, as discussed above, the plurality of nodes can be connected (or disconnected) based on the number of times for the two candidate words concurrently occurring in the same document. In Fig. 3, connecting segments 312, 323, 324, 335 among nodes represent the relevance among nodes. In some embodiments, the connecting segment having a low relevance (i.e., a less important relevance) is represented with a dotted line, and the connecting segment having a high relevance (i.e., an important relevance) is represented with a solid line. The type of connecting segments among nodes can be determined by comparing the relevance with a threshold value.

[0044] In some examples, some candidate word pairs can be omitted from the outburst information network 300, based on the relevance between the various candidate word pairs, and only the remaining candidate words will be taken into consideration. For example, in the network 300 as shown in Fig. 3, since the number of times for the two lexical terms, namely,“Chili” corresponding to the node 303 and“bank” corresponding to the node 305, concurrently occurring in the same document is relatively low (for example, lower than a predefined threshold), it can be determined that the relevance of the connecting segment 335 between the node 303 and the node 305 is low. Accordingly, the connecting segment 335 therebetween can be removed in order to reduce the size of the network, thereby increasing the efficiency of network analysis. As such, at the step of determining the document based on the candidate words, determining one document based on both of the lexical terms respectively corresponding to the node 303 and the node 305 would not be considered. On the contrary, the relevance of the connecting segments 312, 323 and 324 among nodes 301, 302, 303 and 304 may be high. As such, it may be determined that there is a relatively high probability that these nodes belong to a same significant event. Specifically, due to the 7.0 earthquake occurred in Haiti and the 8.8 earthquake occurred in Chili, these lexical terms of“Haiti,”“earthquake,”“casualties” and“Chili” may appear frequently in the news flow during the two period of times, namely,“from January 12 to January 31, 2010” and“from February 27 to March 7, 2010”. In this way, one or more documents including the plurality of lexical terms as mentioned above can be determined, for use in the subsequent digest generation.

[0045] The document used for generating the digest for broadcasting can be determined based on an information outburst network created based on the relevance among the candidate words. In some embodiments, in determining the document associated with at least two candidate words, one or more candidate documents can be first determined, each of which includes at least a pair of candidate words having a relevance, i.e., these candidate words concurrently occur in the candidate document. It can be determined whether the two candidate words has a relevance by determining whether there is a connecting segment between the two nodes corresponding to the candidate words in the information outburst network (for example, the network 300). Then, a sum of PageRank scores of web pages to which the candidate words included in each of the one or more candidate documents belong is acquired. Based on the sum of the PageRank scores, one document can be determined from the one or more candidate documents. The document can be referred to as the so-called“outburst document” because the document comprises several outburst lexical terms at the same time. In this case, a digest can be generated based on the outburst document. The above-mentioned method of identifying the outburst document is also referred to as unsupervised outburst document identification. In some example embodiments, if the sum of the PageRank scores of the web pages to which the plurality of candidate words contained in the document belong is higher than a predefined threshold, it can be determined that the document is an outburst document; otherwise, it is a normal document.

[0046] In some other embodiments, when an outburst document for generating a digest for broadcasting is determined from candidate documents, at least one of the following attributes of candidate words contained in each of the one or more candidate documents can be extracted, including : a lexical term, a maximum value, average value or sum of PageRank scores of the web page to which the plurality of candidate words belong, and a number. Finally, based on the determined attributes as mentioned above, the outburst document is determined from the one or more documents and used in generation of a digest therefrom.

[0047] Identifying the outburst document based on the extracted attribute, as discussed above, can be implemented by pre-training a sorting learning model. Specifically, some historical hot documents are labeled manually by a user, for example, and such document is regarded as an outburst document. For the labeled data, partial order pairs can be created (for the outburst document, non-outburst documents), and for each document, the attributes taken into consideration as discussed above then can be extracted for learning a predetermined sorting learning model (also referred to as training). The sorting learning model obtained by training can be used to determine whether the candidate document is an outburst document based on the above attributes of the candidate document.

[0048] Continuing to refer to Fig. 2, after generating the digest, the digest is broadcast in a speech form at 230.

[0049] In some embodiments, according to a user’s preferences, the user can skip a certain digest being broadcast to him/her using the input device 150 as shown in Fig. 1, or replay the digest having broadcast, by means of the input device 150. In this way, some preferences of the user can be determined after the collecting and analyzing the historical listening behavior of the user. For example, the user may show particular interest in a certain type of lexical terms (for example,“military”), or no interest in a certain type of lexical terms (for example,“entertainment”). As such, for future digest generation for the user, the user’s preferences can be taken into consideration, so as to implement the targeted digest generation. For example, the user’s preferences can be taken into consideration when the documents for generating the digest for broadcasting are filtered, which affect the selection of documents used for generating the digest, for example. Alternatively, or in addition, the user’s preferences can also be taken into account when the digest is generated based on a specific document, which affects specific sentences to be included in the digest, for example.

[0050] In an example embodiment, if a user exhibits a behavior showing that he/she is of extremely no interest in entertainment digests in the past (for example, the user always skip this type of contents), a lexical term (or node) related to entertainment will be labeled in the outburst network 300 as shown in Fig. 3. In this case, although the lexical term related to entertainment probably becomes a hot issue within a period of time, the lexical term (or node) will still not be considered when generating the digest for the user.

[0051] In another example embodiment, if the user has provided feedback (for example by identifying or replaying) on one or more certain digests having been broadcast (for example, through the input device 150), a further digest associated with the digest(s) having been broadcast can be determined. For example, if the user labels or replays a certain piece of news having been broadcast (for example, to indicate that the user probably shows interest in the piece of news), other news related to this one or the follow-ups of this news will be pushed to the user in the future. As an example, for a created play list, the broadcasting of other news related to this news or the follow-ups of this news can be prioritized. As an another example, after a user exhibits interest in a certain piece of news having been broadcast, the generation of a further news related to this news can be prioritized.

[0052] In a further example embodiment, a sequence of pushing the multiple generated digests to the user can be determined or adjusted according to the user’s preferences (and historical access of the user).

[0053] Fig. 4 illustrates a block diagram of a digest generation module 122 according to an embodiment of the present disclosure. As shown in Fig. 4, the digest generation module 122 may include the following modules: a content acquiring module 410, a filtering module 420, a generation module 430, a text/speech conversion module (TTS) 440 and a user profile/behavior module 450.

[0054] The acquiring module 410 is configured to acquire contents from various content sources (such as news websites, blogs, self-media and the like). The filtering module 420 is configured to filter the acquired contents, and for example, acquire one or more candidate words from hot search documents, hot documents or outburst documents, as discussed above. The generating module 430 is configured to generate a digest from the acquired candidate(s) based on a digest template, and the digest template indicates at least one of the following: a word number range of the digest, template elements required for forming the digest, and a semantic relationship among the template elements. The text/speech conversion template 440 is configured to convert the generated digest into a speech form for broadcasting it to the user. The user profile/behavior module 450 is configured to feed regularly user’s preferences or operation behaviors over a past period of time back to the generation module 430.

[0055] Other functions of each module as mentioned above have been described in detail in the embodiment about the digest generating method and are thus omitted herein. It would be noted that the embodiments of the present disclosure are also applicable to any other language, besides Chinese, and the language itself does not constitute any limitation to the scope of the present disclosure.

[0056] Some embodiments of the present disclosure will be provided below.

[0057] In one aspect, there is provided a method of generating a digest for broadcasting, comprising: selecting at least one candidate word associated with an event having a predefined spread extent; generating a digest from the candidate word based on a digest template, the digest template indicating at least the following: a word number range of the digest, template elements required for forming the digest and a semantic relationship among the template elements; and broadcasting the digest in a speech form.

[0058] In some embodiments, selecting the at least one candidate word comprises at least one of the following: selecting the candidate word that has been searched within a predetermined period of time a number of times greater than a first predefined threshold; selecting the candidate word that has been clicked within a predetermined period of time a number of times greater than a second predefined threshold; selecting the candidate word with a frequency of occurrence within a predetermined period of time greater than a third predefined threshold; and selecting the candidate word from an input query item.

[0059] In some embodiments, generating the digest comprises: determining a document containing the candidate word; extracting a sentence from the document; and generating at least one portion of the digest from the sentence based on the digest template.

[0060] In some embodiments, generating at least one portion of the digest from the sentence based on the digest template comprises: in response to determining that the sentence contains the template elements and satisfies the semantic relationship among the template elements, generating the at least one portion of the digest from the sentence.

[0061] In some embodiments, generating the digest comprises: generating at least one portion of the digest from the candidate word based on the digest template.

[0062] In some embodiments, the at least one candidate word comprises a plurality of candidate words, and determining a document containing the candidate word comprises: determining a relevance between any two candidate words of the plurality of candidate words; selecting related candidate words in the plurality of candidate words based on the relevance; and determining the document containing the related candidate words.

[0063] In some embodiments, the relevance comprises: a number of times for two candidate words concurrently occurring in a same document.

[0064] In some embodiments, determining the document containing the related candidate words comprises: determining at least one candidate document containing at least a pair of candidate words in the related candidate words; acquiring a sum of PageRank scores of a web page to which the candidate words contained in each of the at least one candidate document belong; and determining the document from the at least one candidate document based on the sum of the PageRank scores.

[0065] In some embodiments, determining the document containing the related candidate words comprises: determining at least one candidate document containing at least a pair of candidate words in the related candidate words; extracting at least one of the following attributes of candidate words contained in each of the at least one candidate document: a lexical terms, a maximum value, an average value or a sum of PageRank scores of a web page to which the candidate words belong, and a number; and determining the document from the at least one candidate document based on the determined attribute.

[0066] In some embodiments, generating the digest comprises: generating the digest from the candidate word based on the template and user’s preference.

[0067] In some embodiments, generating the digest comprises: generating a plurality of digests; and determining a sequence of broadcasting the plurality of digests to the user based on the user’s preference.

[0068] In some embodiments, generating the digest comprises: determining the digest based on a feedback provided by a user on one or more broadcast digests. [0069] In another aspect, there is provided an apparatus, comprising: a processing unit; and a memory coupled to the processing unit and storing instructions, the instructions, when executed by the processing unit, causing the processing unit to execute acts of: selecting at least one candidate word associated with an event having a predefined spread extent; generating a digest from the candidate word based on a digest template, the digest template at least indicating the following: a word number range of the digest, template elements required for forming the digest, and a semantic relationship among the template elements; and broadcasting the digest in a speech form.

[0070] In some embodiments, selecting the at least one candidate word comprises at least one of the following: selecting the candidate word that has been searched within a predetermined period of time a number of times greater than a first predefined threshold; selecting the candidate word that has been clicked within a predetermined period of time a number of times greater than a second predefined threshold; selecting the candidate word with a frequency of occurrence within a predetermined period of time greater than a third predefined threshold; and selecting the candidate word from an input query item.

[0071] In some embodiments, generating the digest comprises: determining a document containing the candidate word; extracting a sentence from the document; and generating at least one portion of the digest from the sentence based on the digest template.

[0072] In some embodiments, generating at least one portion of the digest from the sentence based on the digest template comprises: in response to determining that the sentence includes the template elements and satisfies the semantic relation among the template elements, generating at least one portion of the digest from the sentence.

[0073] In some embodiments, generating the digest comprises: generating at least one portion of the digest from the candidate word based on the digest template.

[0074] In some embodiments, the at least one candidate word comprises a plurality of candidate words, and determining a document containing the candidate word comprises: determining an relevance between any two candidate words of the plurality of candidate words; selecting related candidate words in the plurality of candidate words based on the relevance; and determining the document containing the related candidate words.

[0075] In some embodiments, the relevance comprises: a number of times for two candidate words concurrently occurring in a same document.

[0076] In some embodiments, determining the document containing the related candidate words comprises: determining at least one candidate document containing at least a pair of candidate words in the related candidate words; acquiring a sum of PageRank scores of web pages to which candidate words contained in each of the at least one candidate document belong; and determining the document from the at least one candidate document based on the sum of the PageRank scores.

[0077] In some embodiments, determining the document containing the related candidate words comprises: determining at least one candidate document at least containing a pair of candidate words in the related candidate words; extracting at least one of the following attributes of candidate words contained in each of the at least one candidate document: lexical terms, a maximum value, an average value or a sum of PageRank scores of a web page to which candidate words belong, and a number; and determining the document from the at least one candidate document based on the determined attribute.

[0078] In some embodiments, generating the digest comprises: generating the digest from the candidate word based on the template and user’s preference.

[0079] In some embodiments, generating the digest comprises: generating a plurality of digests; and determining a sequence of broadcasting the plurality of digests to the user based on the user’s preference.

[0080] In some embodiments, generating the digest comprises: determining the digest based on a feedback provided by a user on one or more broadcast digests.

[0081] In a further aspect, there is provided a computer program product, which is tangibly stored on a non-transient computer readable medium and includes machine executable instructions, the machine executable instructions, when executed, causing a machine to execute acts of: selecting at least one candidate word associated with an event having a predefined spread extent; generating a digest from the candidate word based on a digest template, the digest template at least indicating the following: a word number range of the digest, template elements required for forming the digest and a semantic relationship among the template elements; and broadcasting the digest in a speech form.

[0082] In some embodiments, generating the digest comprises: determining a document containing the candidate word; extracting a sentence from the document; and generating at least one portion of the digest from the sentence based on the digest template.

[0083] In some embodiments, selecting the at least one candidate word comprises at least one of the following: selecting a candidate word that has been searched within a predetermined period of time a number of times greater than a first predefined threshold; selecting a candidate word that has been clicked within a predetermined period of time a number of times greater than a second predefined threshold; selecting a candidate word with a frequency of occurrence within a predetermined period of time greater than a third predefined threshold; and selecting a candidate word from an input query item.

[0084] In some embodiments, generating at least one portion of the digest from the sentence based on the digest template comprises: in response to determining that the sentence contains the template elements and satisfies the semantic relationship among the template elements, generating at least one portion of the digest from the sentence.

[0085] In some embodiments, generating the digest comprises: generating at least one portion of the digest from the candidate word based on the digest template.

[0086] In some embodiments, the at least one candidate word comprises a plurality of candidate words, and determining a document containing the candidate word comprises: determining an relevance between any two candidate words of the plurality of candidate words; selecting related candidate words in the plurality of candidate words based on the relevance; and determining the document containing the related candidate words.

[0087] In some embodiments, the relevance comprises: a number of times for the two candidate words concurrently occurring in a same document.

[0088] In some embodiments, determining the document containing the related candidate words comprises: determining at least one candidate document at least containing a pair of candidate words in the related candidate words; acquiring a sum of PageRank scores of web pages to which candidate words contained in each of the at least one candidate document belong; and determining the document from the at least one candidate document based on the sum of the PageRank scores.

[0089] In some embodiments, determining the document containing the related candidate words comprises: determining at least one candidate document containing at least a pair of candidate words in the related candidate words; extracting at least one of the following attributes of candidate words contained in each of the at least one candidate document: a lexical term, a maximum value, an average value or a sum of PageRank scores of a web page to which the candidate words belong, and a number; and determining the document from the at least one candidate document based on a determined attribute.

[0090] In some embodiments, generating the digest comprises: generating the digest from the candidate word based on the template and a user’s preference.

[0091] In some embodiments, generating the digest comprises: generating a plurality of digests; and determining a sequence of broadcasting the plurality of digests to the user based on the user’s preference.

[0092] In some embodiments, generating the digest comprises: determining the digest based on feedback provided by a user on one or more broadcast digests. [0093] The functionally described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-Programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

[0094] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0095] In the context of this disclosure, a machine readable medium may be any tangible medium that may contain, or store a program for use by or in connection with an instruction execution system, device, or apparatus. The machine readable medium may be a machine readable signal medium or a machine readable storage medium. A machine readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any suitable combination of the foregoing. More specific examples of the machine readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0096] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure. Certain features that are described in the context of separate implementations may also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation may also be implemented in multiple implementations separately or in any suitable sub- combination.

[0097] Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter specified in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method of generating a digest for broadcasting, comprising:

selecting at least one candidate word associated with an event having a predefined spread extent;

generating a digest from the candidate word based on a digest template, the digest template indicating at least the following: a word number range of the digest, template elements required for forming the digest, and a semantic relationship among the template elements; and

broadcasting the digest in a speech form.

2. The method of claim 1, wherein selecting the at least one candidate word comprises at least one of the following:

selecting the candidate word that has been searched within a predetermined period of time a number of times greater than a first predefined threshold;

selecting the candidate word that has been clicked within a predetermined period of time a number of times greater than a second predefined threshold;

selecting the candidate word with a frequency of occurrence within a predetermined period of time greater than a third predefined threshold; and

selecting the candidate word from an input query item.

3. The method of claim 1, wherein generating the digest comprises:

determining a document containing the candidate word;

extracting a sentence from the document; and

generating at least one portion of the digest from the sentence based on the digest template.

4. The method of claim 1, wherein generating the digest comprises:

generating at least one portion of the digest from the candidate word based on the digest template.

5. The method of claim 3, wherein the at least one candidate word comprises a plurality of candidate words, and wherein determining a document containing the candidate word comprises:

determining a relevance between any two candidate words of the plurality of candidate words;

selecting related candidate words in the plurality of candidate words based on the relevance; and

determining the document containing the related candidate words.

6. The method of claim 5, wherein determining the document containing the related candidate words comprises:

determining at least one candidate document containing at least a pair of candidate words in the related candidate words;

acquiring a sum of PageRank scores of web pages to which the candidate words contained in each of the at least one candidate document belong; and

determining the document from the at least one candidate document based on the sum of the PageRank scores.

7. The method of claim 5, wherein determining the document containing the related candidate words comprises:

extracting at least one of the following attributes of candidate words contained in each of the at least one candidate document:

a lexical term,

a maximum value, an average value or a sum of PageRank scores of the web page to which the candidate words belong, and

a number; and

determining the document from the at least one candidate document based on the determined attribute.

8. An apparatus, comprising:

a processing unit; and

a memory coupled to the processing unit and storing instructions, the instructions, when executed by the processing unit, causing the processing unit to execute acts of:

generating a digest from the candidate word based on a digest template, the digest template at least indicating the following: a word number range of the digest, template elements required for forming the digest, and a semantic relationship among the template elements; and

broadcasting the digest in a speech form.

9. The apparatus of claim 8, wherein selecting the at least one candidate word comprises at least one of the following:

selecting the candidate word from an input query items.

10. The apparatus of claim 8, wherein generating the digest comprises:

determining a document containing the candidate word;

extracting a sentence from the document; and

11. The apparatus of claim 10, wherein generating the digest comprises:

12. The apparatus of claim 10, wherein the at least one candidate word comprises a plurality of candidate words, and wherein determining a document containing the candidate word comprises:

determining the document containing the related candidate words.

13. The apparatus of claim 12, wherein the relevance comprises: a number of times for two candidate words concurrently occurring in a same document.

14. The apparatus of claim 11, wherein determining the document containing the related candidate words comprises:

15. A computer program product, which is stored on a computer readable medium and includes machine executable instructions, the machine executable instructions, when executed, causing a machine to execute acts of:

broadcasting the digest in a speech form.