TW201131400A - Information indexing method and system thereof - Google Patents

Abstract

The present invention discloses information indexing method and its system, which is applied to the information indexing system in which every text corresponds to the forward index data to solve the problem of poor indexing efficiency in the existing information indexing technology. The disclosed method includes: receiving inquiry phrases and obtaining the keyword contained in the inquiry phrases through the word segmentation process; looking up the text that matches with said keyword and the forward indexing data corresponding to said text through the backward index data of the information indexing system; ascertaining the abstract of said text according to the forward indexing data corresponding to said text and carrying out the output by using abstract of said text and the text information as the indexing result. By adopting the present invention, the efficiency of information indexing can be increased and simultaneously the accuracy of indexing can be guaranteed to some extent.

Description

201131400 VI. Description of the invention: [Technical field to which the invention pertains] This application relates to a full-text data retrieval in the field of communication, relating to an information retrieval method and an information retrieval system. [Prior Art] With the rapid spread of Internet search engine technology and the rapid development of the road search enterprise, the information retrieval system (also known as the engine) has become more and more popular when people use the Internet. When people use search engines, they usually use the field to input query words, and then get the search results from the search engine backend to form the search results. The three main elements are: title, abstract and web industry commonly known as TAU, which is Title, Abstract' Url three English alphabet abbreviation). Abstract, in terms of the three major factors, it contains the largest amount of information; from the final page, it takes up the largest amount; from the end user experience, its degree determines the correctness of the search results. No, to enable the user to determine the results of the search for the user's needs, a set of high-performance, scalable, easy-to-customize and human-machine interface generation system, is every search engine (ie, information check or The important part of the abstract is that the traditional summary production method is based on the user's input of the full-text data of the search and the basic technology of the search results of the full-text data, especially the Internet-based search for less work is the result of the search. The link (the first in the word, in terms of the effect is enough to summarize the summary. So the show's summary system) does not ask for the word on the instant, through the -5- 201131400 calculation of word frequency, word spacing and other parameters, via text Algorithms such as matching and weighted scoring, and extracting the text paragraph that best matches the query word as a summary, and then will contain the title, abstract and The search result of the address link is returned to the search client for final presentation. Since the traditional search engine is used for information retrieval, matching search is needed in the entire full-text data, and the summary is generated based on the full-text data, because the full-text data is usually The information volume is large, so the retrieval time is long and the retrieval efficiency is low. SUMMARY OF THE INVENTION The embodiments of the present application provide an information retrieval method and system thereof, which are used to solve the problem of low retrieval efficiency in the existing information retrieval technology. The information retrieval method provided by the application is applied to an information retrieval system in which each document corresponds to a positive index data, wherein the positive index data of each document is indexed by each word in the document to record each word. At the location in the document, the method includes the steps of: receiving a query word, obtaining a keyword included in the query word through word segmentation processing; searching for the key through the inverted index data of the information retrieval system a word matching document, and a positive index data corresponding to the document; Determining a summary of the document according to the positive index data corresponding to the document, and outputting the summary of the document and the information of the document as a retrieval result. -6- 201131400 Information retrieval system provided by the application The storage module includes the inverted index data of the system and the positive index data corresponding to each document, wherein 'the index data of each document is indexed by each word in the document to record each word. At the location in the document, the system further includes an input module for receiving a query word input to the system; the search module is configured to perform word segmentation on the query word to obtain a keyword included therein, Searching, by the inverted index data, a document matching the keyword, and a positive index data corresponding to the document; determining a summary of the document according to the positive index data corresponding to the document; And the output module 'is used to output the summary of the document and the information of the document as a search result. The above embodiment of the present application uses the inverted index data to perform information retrieval, obtains a document matching the query word, and uses the positive index data of the document to generate a summary of the document, thereby fully utilizing the positive index data of the document. Compared with the full-text data, the content description has certain accuracy characteristics. Compared with the search results obtained by the traditional information retrieval system through full-text search, the efficiency of query matching search can be improved, and the summary of the summary is generated. Efficiency' also ensures the accuracy of the search results and the resulting summary to a certain extent. [Embodiment] Hereinafter, embodiments of the present application will be described in detail with reference to the accompanying drawings. 201131400 The information retrieval method provided by the embodiment of the present application is applied to an information retrieval system. The information retrieval system not only saves the inverted index data of the document (that is, the full-text data, the same below), but also provides each document. Corresponding independent index data. In the current general information retrieval system, 'usually contains inverted index data of a collection of documents, including each

The document corresponds to a separate positive index data (such as the generic postgresQL' open source software full-text search system). For an information retrieval system that does not include a positive index data for each document, a variety of technical solutions can be used to obtain an independent index data of each document from the inverted index data of the entire document collection. It is done offline, but it does not affect the performance of the information retrieval service provided by the information retrieval system. The inverted index data of the so-called document collection is the index of the document created based on the word. For example, each word is a record in the database. , the word as a keyword 'followed by the document identification ID, location and other information. Suppose there are 3 documents: filel, file2, and file3. The contents of the document are as follows: filel (word 1, word 2, word 3, word 4...·) file2 (word a 'word b, word c, word d, word a , the word c, the word d·.··) file3 (word 1, word a, word 3, word d.._.) The inverted index data of the document set consisting of the above documents includes: word 1 ( filel, file3 ) , word 2 ( filel ), word 3 ( filel , file3 ), word a ( fi 1 e2, fi 1 e3 ) and so on. The so-called index data of the document is the index of the word position based on the word -8-201131400 included in the document. For example, for the above file2 document 'if the position where each word appears in nle2 is denoted by p〇sl,p〇s2 ..., then its positive index data is: file2 (word a: ps〇l, pos5; word b: p 〇s2; the word c · pos3, pos6; the word d: pos4, pos 7 ··) Visible 'According to the document and its inverted index data, you can get the index data of each document. In order to facilitate the data maintenance and management of the 'slot collection and its inverted index data' and the independent index data of each document can be organized in the form of a database. Of course, it can also be organized in other forms, such as file format. . The embodiments in the present application are described by taking a collection of documents organized in the form of a database and independent indexed index data of each document as an example. Referring to FIG. 1 ′ is a schematic flowchart of an information retrieval system in an embodiment of the present application for providing an information retrieval service for a user. The information retrieval system in the following process includes a full-text database storing the full-text data of the document, and an inverted index library storing the inverted index data and a positive index library storing the corresponding index data of each document, the full text The database and the positive index library 'and the full-text database and the inverted index library are associated with each other through a defined index (such as 'document ID'), so that the positive index of each document and its document - corresponding" based on the above information retrieval system The information retrieval process includes: Step 101 'The information retrieval system accepts the query words submitted by the user. The information retrieval system can provide users with an information retrieval interface. The interface can provide users with input query words and operation commands for submitting query words to the information retrieval system. The query word input by the user may be a word, a phrase composed of a plurality of words, or a plurality of words (or phrases) connected by a relational operator (and, or, etc.). Step 102: The information retrieval system performs word segmentation on the query words submitted by the user, obtains all the keywords included in the query words, and then finds the documents matching the keywords by inverting the index data in the inverted index database, and According to the found documents, the positive index data corresponding to the documents are found from the positive index library, and the summary of each document is generated according to the positive index data of each document, and the generated document summary and the title of the corresponding document, Information such as Url is used as a search result. In this step, after the information retrieval system determines the documents matching the keywords, the corresponding index data of the respective documents can be obtained. Since the information retrieval system can retrieve a plurality of documents, in order to improve the usability of the information retrieval result for the user and improve the user experience, the information retrieval system can sort the retrieved documents according to the matching degree of the document and the keyword from high to low. , thereby obtaining a corresponding sequence of document IDs. Step 103: The information retrieval system outputs the search result according to the generated search result and the document ID sequence, and the search result may be presented in a search result interface provided by the information retrieval system, and the search result may include: a title, a Url, and a document summary of the document. The document abstract usually contains keywords, preferably 'can be the part of the document that matches the keyword with a high degree of content. In this step, the information retrieval system can be obtained according to the previous step to -10 201131400. Document ID sequence, obtain the title of the corresponding document, Url and other information from the full-text database (in the normal case, the full-text index library can use the document ID, title, Url, etc. as the index data of the full-text content of the document), of course, if The index library also contains information such as document title and Url, and can also be obtained directly from the front index library, and then output or display in the order of the document ID sequence. The information retrieval system also specifies the format of the output search results and the length of the document summary. The information retrieval system, as shown in FIG. 2, may include the following functional modules: an input module 2 1 , a retrieval module 22 , and an output module 23 , and further includes a full-text database 24 , an inverted index library 25 , and a positive index library 26. The full-text search database 24 stores the full-text data of each document, and can store the full-text data of the document by using the document ID, the document title, and the Url as an index; the inverted index database 25 stores the inverted index data; The library 26 stores the respective positive index data of each document. The functions implemented by each module can correspond to the corresponding steps in the above process. The search module 22 may further include a search sub-module 22 1 , a summary generation sub-module 222 , and a result submission sub-module 2 2 3 ° . The information retrieval system shown in FIG. 2 is further combined with the information retrieval process. A detailed description. After receiving the query word submitted by the user, the input module 2 1 submits the query word to the search module 22; the search module 22 performs word segmentation on the query word to obtain a keyword, and the inverted index database 2 according to the keyword Searching in 5 to retrieve the document matching the keyword, and then generating a summary of the document according to the positive index data of the corresponding document in the index library 26, and then including -11 - 201131400 with the document summary search The result is submitted to the output module 23, wherein if the title and Url of the document need to be output, the document title and Url can be further obtained from the full-text database 24 or the inverted index database 25; the output module 23 receives the search result. The search result is output. For each document matching the keyword, the retrieval module 22 can create a positive sequence of all words and a positive index including only keywords by scanning each word in the corresponding positive index data in the positive index library 26. The sequence, and then determining the start and end positions of the document summary text segment in the positive or full-text data based on the two sequences, thereby obtaining a document summary. The specific implementation process of the information retrieval function of the retrieval module 22 requires some variables, and the variables may include: a document ID sequence: an ID for storing the retrieved document; eight 1*1: "_4 array: used to store and retrieve The positive sequence of all the words contained in the document. The positive sequence records the text of each word and its position in the full-text data of the document. It can be expressed in the form of word text and location list, for example:

The Array_A record has (posl:wordl, pos2:word2, pos3:word3, pso4:wordl...) where pos represents the character position of the first character of the corresponding word text in the full-text data, and word represents Word text. That is, Array_A* records the text of each word with the word position as an index.

Map_A array: used to store the positive index sequence of each keyword obtained after the word segmentation processing of the query word, the positive index sequence records the position of each keyword appearing in the full text of the corresponding document, and can use the red black tree -12- 201131400 Structure (Red-black tree is a specific type of binary tree, a structure used in computer science to organize data such as digital blocks. All data blocks are stored in nodes.) Organize and store keywords Text and location linked list, for example, if wordl and word2 in Array_A above are keywords, then

The Map_A record has (word 1 : pos 1, pos4; w〇rd2 : p〇s2...) where pos represents the character position of the first character of the corresponding keyword text in the full-text data, keyword Represents the keyword text. That is, in Map_A, the word text (the word is a word that matches the keyword) is used as an index to record the position of each word.

Res_Beg, Res_End: used to indicate the starting and ending position of the summary text segment.

Best_Path: used to represent the best summary path, including the text start and stop position parameter, through which the corresponding text segment can be defined in the positive index data or the full text data, and the text segment defined by the best summary path is The matching degree of keywords is higher than that of other text segments; RL_ is the length of the summary text, usually expressed by the number of words, which can be assigned when the system is initialized. The information retrieval process of the retrieval module 22 can be divided into two stages: a document retrieval stage to retrieve a document matching the keyword (such as a document containing a keyword or a word having a meaning equivalent to the keyword). Document); a summary generation stage to generate a corresponding summary for the retrieved document. In the document retrieval phase, the retrieval sub-module 2 2 1 performs word segmentation processing on the query -13-201131400 words submitted by the user, and obtains all the keywords included in the query word. Then, a matching search is performed in the inverted index library 25 to find the IDs of the documents matching the keywords, and the document IDs are sorted in descending order of matching degree, and the sorted document IDs are stored as documents. ID sequence. The word segmentation process performed in this stage can be implemented by various techniques, such as the existing Chinese word segmentation processing technology; the matching search operation in the inverted index library 25 can be implemented by the existing full-text search technology. It should be understood that the word segmentation techniques and full-text search techniques used in the embodiments of the present application do not limit the scope of protection of the present application. In the summary generation stage, the summary generation sub-module 222 traverses the positive index data corresponding to each document ID in the forward index library 26 according to the document ID recorded in the document ID sequence. For each document ID corresponding to the positive index data, for each word traversed, record the position of the word and the text of the word, put into the array Array_A (usually recorded into the array data structure), if the word As with the keyword, the text of the word and the location of the word are also recorded in the collection Map_A*. After traversing the positive index data of the document, if the total length of all the word text recorded by Array_A does not exceed RL, the best summary path is the starting position of the first word in Array_A to the end of the last word: Otherwise, traverse the word text and position list recorded in Map_A to find the corresponding positions Res-Beg and Res_End of the start and end words of the shortest text segment that meet the condition, then the best summary path from Res_Beg to Res_End in Array_A; After determining the best summary path, the summary generation sub-module 222 locates the corresponding text segment as the summary text from the corresponding positive index data or the full-text data according to the determined best summary path, and Submit it to the result submission sub-module 2 2 3 . The above conditions for satisfying the best summary path may include:

Res—The length of the text between Beg and Res_End is rl, or does not exceed RL;

The number of keywords included between Res_Beg and Res_End is the largest. If there are multiple summary paths that meet the above two conditions, the summary path with the largest sum of repeated keywords in the summary text defined by these summary paths is used as the best summary path. The result submission sub-module 223 queries the corresponding document title, Url, etc. from the full-text database 24 or the inverted index library 25 according to each document ID recorded in the document ID sequence, and submits the information to the output module together with the summary text. The 23 ° output module 23 can be configured with a summary formatting parameter Fmt_Arg (English abbreviation for Format Argument). The output module 23 formats the summary text, the document title, the Url, and the like according to the parameter Fmt_Arg. Then, according to the order of the document IDs in the document ID sequence, the related information of the corresponding document (including the title, Url, and abstract) is specified. The format display output, preferably, is displayed in the HTML (Hypertext Markup Language) page markup language common to the World Wide Web, providing users with eye-catching and recognizable search results for a better user experience. The flow of the summary generation sub-module 222 traversing the positive index data of the document can be as shown in FIG. 3A, and the flow of generating the best summary path after the traversal is completed can be as shown in FIG. 3B. The summary generation sub-module 222 traverses the positive index data corresponding to each document ID -15-201131400 in the document ID sequence, as shown in FIG. 3A, when the summary generation sub-module 222 obtains from the document ID sequence. After a document ID, the corresponding positive index data is found from the positive index library 26, and the following step 301 is performed to traverse the current index data by word by word; step 302, whether to traverse the word, if yes, execute Step 3〇3; otherwise, go to step 3 06. In this step, there are two cases in which the word is not traversed: it has been traversed to the end of the current index data, that is, the traversal of the current data has been completed; or, the current index data is empty. . Step 3 03, the text of the word and the current position of the word recorded in the current index data are recorded into Array__A, and the start and end positions of each word can be determined through the record in Array_A; for example, for "computer" The Chinese word has a starting position corresponding to the position of the word "calculation", and its ending position is the position corresponding to the word "machine"; and for words in English or other words containing a word separator, it can be passed through the single stomach The stomach separator determines the start and end position of a word; Step 3 04, whether the word is the same as the keyword, if yes, then perform step 3 0 5; otherwise, return to step 3 0 1, continue to traverse the word Other words; Step 3 05, record the text of the word and the current position of the word recorded in the current index data into Map_A. After the digest generation sub-module 222 traverses the completion of the positive index data, the best excerpt-16-201131400 path is determined according to the Array_A and Map_AM recorded in the traversal process, and the best summary path parameter is assigned to the Best_Path. The process may be as shown in FIG. 3B, including the following steps: Step 310, assigning the variable N to 0, and assigning the Best_Path to null; Step 311, according to Map-A, taking the position corresponding to one of the word texts値 give Res_Beg, and then determine the position of a word text from Array_A according to RL, so that the text segment of length RL defined from ReS_Beg to the determined position 包含 contains a keyword, if such a position can be obtained値, proceed to the subsequent step 3 1 2; if such a position cannot be obtained, then step 3 1 6 is performed; in step 312, the determined position endowment is given to Res_End, so that Res _B eg to Re s_ End The length of the text between the two does not exceed RL. Preferably, according to the record of Array_A, the position corresponding to Res_Beg is the start position of the word recorded in Array_A (that is, the first character of the word text). Position), the position corresponding to Res_End is the end position of the word recorded in Array_A (that is, the position of the last character of the word text) to ensure that the text segment content between Res_Beg and Res_End is clear and complete; Step 313, Determine the number of keywords included in the text segment between Res_Beg and Res_End according to Map_A, and assign the quantity to the variable η; Step 3 1 4, η is greater than Ν, and if yes, perform step 3 1 5; otherwise, return to step 31 to determine the next different Res_Beg; Step 3 1 5, η η end to N, η to zero, record the current Res_Beg and Res_End_ to the Best Path, and return Step 3U '201131400 to determine the next different Res_Beg; Step 3 16, the current best path of Best_Path is the best summary path, and output the Best_Path. As can be seen from the flow shown in FIG. 3B, the digest generating sub-module 2 2 2 passes through multiple loop operations 'each time taking a different position endowment to Res_B eg ' and the text segment defined in the current Res_Beg and Res_End When the number of key items included in the maximum number is recorded, the current Res_Beg and Res_End are recorded to the Best_Path, so that the text segments defined by Res_Beg and Res_End recorded in the final Best_Path contain the most keywords, thereby obtaining the best summary path. It should be noted that in the process of determining the best summary path, after completing a positive index data per traversal, the traversal result can be used to immediately determine the best summary path of the index data; or all the positives can be traversed. After indexing the data, 'the best summary path is determined according to the traversal results of the positive index data. The following takes the information retrieval system applied to the Internet as an example, and further describes the implementation process of the embodiment of the present application through a specific example. When a new webpage is published in the Internet, the content of the webpage is indexed. The data is stored in the positive index database of the information retrieval system, and a correspondence relationship with the webpage identifier is established. In this example, the newly published webpage and its corresponding positive index data are: Webpage document 1: ID=1〇〇, titled "Security technology applied to the computer"; -18- 201131400 Corresponding positive The index data is: (computer: 2 ' 5 0, 90; security: 25...), the length of which is 1〇〇 word length; indicates the 2nd and 50th of the full text of the web document 1 At the position of 90 characters, the word "computer" appears in the word "safe" at the position of the 25th character; Web document 2: ID = 2 00, titled "How to improve the security of the computer"; Corresponding positive index data: (computer: 1 〇, 70: security: 15...) 'The length is 100 words; it means the 10th, 70th character position of the full text of the web page 2 The word “computer” appears as the word “safe” at the 15th character position. The inverted index data includes: computer (ID 1 00, ID 200), security (ID 100, ID 200)... The data format of the search result specified by the information retrieval system is HTML format, and the length of the summary text does not exceed 50 words. When the system receives the query word "computer security submitted by the user, it performs word segmentation processing to obtain the keywords "computer" and "security"; according to the inverted index data matching query, it finds that the above keyword is included The webpage document is a webpage document with ID=100 and ID=200, and then the corresponding positive index data is determined according to the webpage document, because the number of occurrences of the keyword in the webpage document with ID=1〇〇 is 4, The number of occurrences of the webpage document with ID = 200 is 3 ', the former is considered to match the query word more; then, the two positive index data are traversed separately: the index data of the web document ID = 1 00 After the traversal, you can get: -19- 201131400

Array_A :(...2 : computer;...25: security; ...90: computer;...)

Map_A : (computer: 2, 50, 90; security: 25) According to the obtained Array_A and Map_AU, the text segment with the start position 2 and the end position 52 contains 3 other text segments of length 50. There are many keywords, the starting and ending position of the book [2, 5 2] is the same as the best summary text path. The index data of the web document ID = 200 can determine the best summary text path is [1, 50 Then, the system presents the webpage Url and the abstract of the webpage ID=100 and the webpage ID=200 as the search result according to the determined summary text of the best summary text and according to the degree of matching of the document with the query word ί HTML format. user. The embodiment of the present application further provides the foregoing technical solution, that is, when the digest is generated, instead of traversing the positive line, the summary path is obtained, and the path of the full-text data of the document is traversed, and correspondingly according to the obtained abstract. The summary text is extracted from the path, and the specific traversal process and the summary path are similar to the foregoing description, and are not described herein again. Compared with the conventional technical solution, the technical solution provided by the embodiment of the present application only needs to perform word segmentation processing on the query word, and needs to perform word segmentation processing on the query word and the full text data. The specific technical solution provided by the embodiment of the present application utilizes the text 50: the computer; the step is derived from the keyword, and the traversal result path of the segment* is generated to generate an average height, and the title of the page is replaced by The program has obtained the information retrieval from the method of determining the full-text path. However, the traditional method can be used to generate the abstract of the document, but the traditional way The full-text data of the document needs to be used to generate the abstract of the document, and the index data of the document is less than the amount of data contained in the full-text data of the document, so that the efficiency can be improved, and on the other hand, the information of the positive index of the document can be used. In order to conveniently count the frequency, number of times, etc. of words, it is convenient and accurate to determine the summary of the document' It is more accurate and comprehensive to include the content of the corresponding full-text data. Therefore, using the positive index data of the document to generate the document abstract can guarantee the rationality and accuracy of the search results to a certain extent. The example makes full use of the existing full-text index structure of the information retrieval system, and fully takes into account the existing form of retrieval results, so that it can improve the production efficiency by using more accurate and more targeted data when generating the abstract, and Improve user satisfaction for the end result. The information retrieval system provided by the embodiment of the present application has the characteristics of high cohesion and loose coupling, and is easy to integrate with various existing information retrieval systems. Exceptionally, the system is also characterized by high performance, scalability, and customization. The present application is described with reference to flowchart illustrations and/or block diagrams of a method, apparatus (system), and computer program product according to an embodiment of the invention. It will be understood that each flow and/or block of the flowcharts and/or <RTIgt; These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer 'embedded processor or other programmable data processing device to generate a machine that is generated by instructions executed by a processor of a computer or other programmable data processing device. A device for implementing the functions specified in a flow or a flow of a flow chart and/or a block diagram of one block or multiple blocks - 21 - 201131400. The computer program instructions can also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a particular manner such that instructions stored in the computer readable memory are generated including the instruction device. Manufactured, the instruction means implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart. These computer program instructions can also be loaded onto a computer or other programmable data processing device to perform a series of operational steps on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more flows of the flowchart or in a block or blocks of the flowchart. It will be apparent to those skilled in the art that various modifications and changes can be made in the present application without departing from the spirit and scope of the application. Accordingly, it is intended that the present invention cover the modifications and variations of the invention, and the scope of the invention is intended to be included in the scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a schematic flow chart of information retrieval in an embodiment of the present application; FIG. 2 is a schematic structural diagram of an information retrieval system in an embodiment of the present application; FIG. 3A and FIG. 3B are embodiments of the present application; Schematic diagram of the processing flow of the retrieval module of the information retrieval system. -22- 201131400 [Key component symbol description] 2 1 : Input module 22 : Search module 2 3 : Output module 24 : Full-text database 25 : Inverted index library 26 : Positive index library 221 : Search sub-module 222: Summary generation sub-module 223: result submission sub-module

Claims (1)

201131400 VII. Scope of application for patents: 1. An information retrieval method, which is applied to an information retrieval system in which each document corresponds to a positive index data, wherein the positive index data of each document is based on each word in the document. Indexing to record the position of each word in the document, characterized in that the method comprises the steps of: receiving a query word, and obtaining a keyword included in the query word through word segmentation processing: through the information retrieval system Querying the index data to find a document matching the keyword, and the positive index data corresponding to the document: and determining a summary of the document according to the positive index data corresponding to the document, and summarizing the document and the document The information is output as a result of the search. 2. The method of claim 1, wherein the summary determined according to the positive index data is: according to all the text segments determined by the positive index data not exceeding the prescribed length, The text segment with the most occurrences of the keyword. 3. The method of claim 2, wherein if the text segment that does not exceed the specified length and the keyword has the most occurrences is plural, the text segment in which the keyword is repeated most frequently is used as a summary . 4. The method of claim 2, wherein the process of determining the digest of the document based on the positive index data corresponding to the document is specifically: for each document in the document, performing the following Steps: traverse the words in the index data of the document one by one, and record each traversed word in the first capital -24-201131400 material structure with the word position as an index, and when traversing the word and the key When the word matches, the position of the word is recorded by using the word as an index in the second data structure; determining the starting position of the digest according to the record of the second data structure; according to the specified length of the digest and the record of the first data structure Determining the end position of the digest; the length of the text segment between the start position and the end position does not exceed the specified digest length, the keyword appears most frequently in the text segment: and according to the determined digest A summary of the document is generated from the start and end positions and the index data. The method of any one of claims 1 to 4, wherein the summary of the document and the information of the document are output as a retrieval result, specifically: matching the keyword according to the document The order of the document is sorted from the highest to the lowest, and the information of the document is sorted as a search result, and the sorted search result is output according to the specified data format. 6 - an information retrieval system includes a storage module storing the inverted index data of the system and the positive index data corresponding to each document, wherein the positive index data of each document is made with each word in the document Indexing to record the position of each word in the document, further comprising: an input module 'for receiving a query word input to the system; a search module' for performing word segmentation on the query word Obtaining the keyword 'through the inverted index data to find the document matching the keyword' and the positive index data corresponding to the document; according to the positive index data corresponding to the file - 25- 201131400 file And determining a summary of the document; and an output module for outputting the summary of the document and the information of the document as a retrieval result. 7. The system of claim 6, wherein the retrieval module determines, according to the positive index data corresponding to the document, the summary of the document, and the total determined according to the positive index data does not exceed In a text segment of a specified length, the text segment with the most occurrence of the keyword is used as a summary of the document. 8. The system of claim 7, wherein if the search module determines that the text segment does not exceed the specified length and the keyword has the most occurrences, the keyword is repeated. The most frequently occurring text segment is used as a summary. 9. The system of claim 6, wherein the search module comprises: a search sub-module for performing word segmentation on the query word to obtain a keyword contained therein, through the inverted row Indexing data to find a document matching the keyword, and a positive index data corresponding to the document; a summary generating sub-module for traversing each of the positive index data corresponding to each document in the document Words, in the first data structure, using the position of the word as an index to record each traversed word, and when the traversed word matches the keyword, the word is recorded as an index in the second data structure. The position of the word: and, according to the record of the second data structure, determining the starting position of the digest, determining the end position of the digest according to the specified length of the digest and the record of the first data structure, the start -26- 201131400 The length of the text segment between the position and the end position does not exceed the specified summary length, and the keyword appears most frequently in the text segment; A summary of the documents produced and the starting and ending position from the forward index data Abstract; and the submission of sub-module for the resulting document is a summary of the information and the documents submitted to the output module. 10. The system according to any one of claims 6 to 9, wherein the output module outputs the summary of the document and the information of the document as a retrieval result, according to the document and the key The order of the word matching is from high to low, the summary of the document and the information of the document are sorted as search results, and the sorted search results are output according to the specified data format. -27-