Patent Application
20210089622
A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
The present invention generally relates to the field of natural language process (NLP). More specifically, the present invention relates to techniques of extracting sentences from a document for generating a summary of the document.
Text summarization is one of the difficult tasks in the field of natural language processing (NLP). With the number of electronically-accessible documents now greater than ever before, the overwhelming amount of textual information makes it difficult for readers to quickly decipher all relevant information thereof. Text summarization tools may provide one solution for such reading problem. The goal of text summarization is to shorten a text passage containing a lot of pieces, such that reader can identify the main message of the text passage quickly and efficiently. One of methods is based on selecting the most important sentences from the text passage. That is, the selected sentences are not modified, but remain the same. In other words, the summarized text is not in a rewritten form, but a selection of a sub-group of the original sentences among the group of all sentences composing the text passage. However, determining the relative importances of sentences is a highly complex process, and accuracy remains a much studied and wanted issue in the current state of the art.
In view of the above, the present invention provides an apparatus and method for summarizing text with sentence extraction. In accordance to one aspect of the present invention, a plurality of sentences are extracted from a text passage or document by a NLP based feature extractor. A word vector set with respect to each of the sentences is generated by a processor. The word vector set with respect to each of the sentences is used to generate a n-grams vector set and a phrase-n vector set with respect to each of the sentences by the processor, in which n is a positive integer greater than 1. A word score representing degree of similarity between the word vector sets, a n-grams score representing degree of similarity between the n-grams vector sets, and a phrase-n score representing degree of similarity between the phrase-n vector sets are computed by the processor. The word score, the n-grams score, and the phrase-n score are combined to compute an edge score representing degree of similarity between the two sentences by the processor. Text features are selected from the sentences using the edge scores of the sentences, so as to output a summary of the document.
The present invention provides an apparatus for summarizing text with sentence extraction including an NLP based feature extractor and a processor. The NLP based feature extractor is configured to extract a plurality of different sentences from a document including text. The processor is configured to generate a word vector set with respect to each of the sentences and use the word vector set with respect to each of the sentences to generate a n-grams vector set and a phrase-n vector set with respect to each of the sentences, in which n is a positive integer greater than 1. The processor is further configured to compute a word score representing similarity between the word vector sets, a n-grams score representing similarity between the n-grams vector sets, and a phrase-n score representing similarity between the phrase-n vector sets, and the word score, the n-grams score, and the phrase-n score are combined to compute an edge score representing similarity between the two sentences. The processor is further configured to select text features from the sentences using the edge scores of the sentences, so as to output a summary of the document.
The advantages of the present invention include: better performance for text summarization due to its determination of degree of similarity between sentences by defining edge connection thereof; a solution to handle different types of text (i.e. same wording and synonyms); and a solution to handle extra word effect and word ordering effect.
Embodiments of the invention are described in more detail hereinafter with reference to the drawings, in which:
In the following description, natural language processing (NLP) methods and apparatuses for summarizing text with sentence extraction, and the likes are set forth as preferred examples. It will be apparent to those skilled in the art that modifications, including additions and/or substitutions may be made without departing from the scope and spirit of the invention. Specific details may be omitted so as not to obscure the invention; however, the disclosure is written to enable one skilled in the art to practice the teachings herein without undue experimentation.
The present invention provides a method and an apparatus for summarizing text, which can be incorporated in various NLP methods and systems. In accordance to one embodiment of the present invention, provided is a method that integrates an extraction stage, a scoring stage, and a rank stage. The stage-integration comprises a disassembly of an NLP-based summarizing system into at least two logical components: a feature extractor and a processor.
In accordance to one embodiment, the feature extractor is an NLP-based feature extractor. At design time, the feature extractor is trained with a training dataset containing words and characters of a selected language. During training, a stop-words database is constructed such that the feature extractor is trained to recognize stop words (e.g. a commonly used word, such as “the”, “a”, “an”, “or”, “and”, “he”, “she”, or the likes). The stop-words database may be implemented in one or more databases and/or file systems local or remote to the feature extractor's run-time execution computing devices and/or processing servers.
In accordance to one embodiment, the processor is trained to generate vector space by mapping words or phrases from a set to vectors of real numbers. The training of the processor constructs a feature vector database. The feature vector database may be implemented in one or more databases and/or file systems local or remote to the processor's run-time execution computing devices and/or servers. The processor is configured to map words extracted from a sentence by the feature extractor to word vectors, so as to generate a word vector set. The processor is further configured to execute calculation in linear algebra, such as calculation in vector or matrix. For example, if word vectors in a first sentence span a first space and word vectors in a second sentence span a second space, principal angles between the first space and the second space found by singular value decomposition (SVD) are summed up to achieve the calculation.
As shown in
In the exemplary illustration of
After the word processing S10, the first word set 212 and the second word set 214 are fed to the processor 120 for starting with the operation S20. The set formation S20 is executed by the processor 120 to generate a word vector set, a n-grams vector set, and a phrase-n vector set with respect to each of the first word set 212 and the second word set 214 (i.e. with respect to each of the first sentence 202 and the second sentence 204), in which n is a positive integer greater than 1. For example, the n-grams vector set may be set as a 2-grams or 3-grams vector set.
In one embodiment, a first word vector set 221 is generated by using word embedding to convert each of the words of the first word set 212 into a high dimensional vector. That is, each of the words of the first word set 212 may serve as a first feature vector to generate a first word vector set 221. In this regard, since the number of the words of the first word set 212 is four (i.e. in the afore-described illustrative example, “Mathematics”, “exam”, “coming”, and “soon”), the first word vector set 221 may have the four first feature vectors which can be respectively labeled as a sequence of numbers with positive integers, such as the first word vector set 221 is {“Mathematics”, “exam”, “coming”, “soon”}={{right arrow over (a)}1, {right arrow over (a)}2, {right arrow over (a)}3, {right arrow over (a)}4}. By converting sentences into word vector sets, it is particularly advantageous in solving different types of text (i.e. same wording and synonyms) issue. Thereafter, a first n-grams vector set 222 and a first phrase-n vector set 223 can be generated by using the first word vector set 221.
In the generation of the first n-grams vector set 222, each of second feature vectors thereof is generated by a summation of the consecutive n first features. For example, if the first n-grams vector set 222 is a 2-grams vector set, each of the second feature vectors of the first n-grams vector set 222 is generated by a sum of the consecutive two first feature vectors of the first word vector set 221. That is, the first n-grams vector set 224 that serves as a 2-grams vector set may have three second feature vectors, such that the first n-grams vector 224={{right arrow over (c)}1, {right arrow over (c)}2, {right arrow over (c)}3}, and each of the second feature vectors satisfies: {right arrow over (c)}i={right arrow over (a)}i+{right arrow over (a)}i+1. More specifically, the first n-grams vector set 222 is generated by combining information of the consecutive two words of the first word set 212. In the afore-described illustrative example, the first n-grams vector set 222 is {“Mathematics exam”, “exam coming”, “coming soon”}. This is particularly advantageous in solving extra word(s) issue.
In the generation of the first phrase-n vector set 223, each of third feature vectors thereof is generated by concatenating the consecutive n first feature vectors in the same column. For example, if the first phrase-n vector set 223 is a phrase-2 vector set, each of third feature vectors of the first phrase-n vector set 223 is generated by concatenating the consecutive two first feature vectors of the first word vector set 221 in the same column. That is, the first phrase-n vector set 223 that serves as a phrase-2 vector set may have three third feature vectors, such that the first phrase-n vector set 223={{right arrow over (e)}1, {right arrow over (e)}2, {right arrow over (e)}3}, and each of the third feature vectors satisfies:
More specifically, the first phrase-n vector set 223 is generated by combining feature vectors of the consecutive two words of the first word set 212, hence the dimension of the first phrase-n vector set 223 is double of that of the first word vector set 221. This is particularly advantageous in solving word order issue.
In one embodiment, a second word vector set 226 is generated in the same manner as that of the first word vector set 221. In the afore-described illustrative example, the second word vector set 226 has five fourth feature vectors due to the second word set 214 having five words (i.e. “Peter”, “Mary”, “revision”, “Mathematics”, “together”), such that the second word vector set 226={“Peter”, “Mary”, “revision”, “Mathematics”, “together”}={{right arrow over (b)}1, {right arrow over (b)}2, {right arrow over (b)}3, {right arrow over (b)}4, {right arrow over (b)}5}. Similarly, a second n-grams vector set 227 can be generated in the same manner as that of the first n-grams vector set 222 to have four fifth feature vectors, such that the second n-grams vector set 227={{right arrow over (d)}1, {right arrow over (d)}2, {right arrow over (d)}3, {right arrow over (d)}4}, and a second phrase-n vector set 228 can be generated in the same manner as that of the first phrase-n vector set 223 to have four sixth feature vectors, such that the second phrase-n vector set 228={{right arrow over (f)}1, {right arrow over (f)}2, {right arrow over (f)}3, {right arrow over (f)}4}.
After generating the set formation, the operations S30 is executed by the processor 120. Reference is made to
In the operation S30, degrees of similarity between the first and second word vector sets 221, 226/n-grams vector sets 222, 227/phrase-n vector sets 223, 228 are determined in accordance with a word score 232, a n-gram score 234, and a phrase-n score 236 respectively. The word score 232, the n-gram score 234, and the phrase-n score 236 are computed individually, as stated below.
In step S302, the first word vector set 221 and the second word vector set 226 are processed to respectively generate a first matrix and a second matrix that represent a first space spanned by the first word vectors set 221 and a second space spanned by the second word vector sets 226 respectively. In step S304, the first matrix and the second matrix are processed by performing orthogonalization on the first matrix and the second matrix. In step S306, the orthogonalized first and second matrices are processed to derive the matrix multiplication from the orthogonalized first and second matrices. In one embodiment, the matrix multiplication is generated with transpose. For example, if the orthogonalized first and second matrices are Q and P, the matrix multiplication can be expressed as QTP.
Thereafter, in step S308, the matrix multiplication is processed by performing SVD thereon. With the SVD, the matrix multiplication QT is derived to satisfy:
where all σi are referred to as angles between the first and second spaces spanned by the first and second word vector sets 221 and 226. That is, with the SVD, diagonal elements of a diagonal matrix Σ are determined. In step S310, the diagonal matrix Σ is processed by summing up the diagonal elements of the diagonal matrix Σ, thereby computing the word score 232. In one embodiment, the sum of the diagonal elements is normalized. For example, the word score 232 is derived by an equation:
where s1 is the word score 232. Since σi, which are defined as the angles as above, can represent the degree of similarity of the spanned first and second spaces, the word score 232 derived according to σi represents the degree of similarity between the first and second word vector sets 221 and 226.
By using the same computation as that for the word score 232 of the first and second word vector sets 221 and 226, the n-gram score 234 representing the similarity between the first and second n-grams vector sets 222, 227 and the phrase-n score 236 representing the similarity between the first and second phrase-n vector 223 and 228 respectively can be computed as well.
Referring to
In one embodiment, the edge score is derived by an equation:
where ES is the edge score, s1 is the word score, s2 is the n-grams score, s3 is the phrase-n score, and coefficients α, βn, and γn are positive. In this regard, the coefficients α, βn, and γn serve as the weighting numbers for each individual score respectively, and the sum of the coefficients α, βn, and γn is equal to 1. The parameters n and K are defined by the n-gram vector sets and the phrase-n vector sets. In one embodiment, the n-gram vector sets are 2-gram vector sets and the phrase-n vector sets are phrase-2 vector sets, and both of the parameters n and K are “2” and coefficients α, βn, and γn can be set as α=0.6, β2=0.3, and γ2=0.1. In another embodiment, if 3-grams vector sets are used, 2-grams vector sets and 3-grams vector sets would be constructed and the parameters n and K are set as n=2 and K=3.
Accordingly, edge scores between all sentence pairs in the document 200 are computed. For example, if the document 200 contains x sentences, from these x sentences,
number of edge scores are computed. The edge scores then correlates with the importances of the sentences of the document 200.
In an exemplary illustration aided by
Referring to
where the x sentences are labeled s1 to sx, and an edge score of any two of the sentences is edge(si,sj). For example, edge(s1,s2) and edge(s2,s1) are the same edge score of the sentences s1 and s2. Furthermore, the diagonal elements of the matrix G are 0's, because the same sentence (e.g. s1 and s1) has no edge connection itself and hence the edge score thereof is 0.
In one embodiment, normalization is performed on each of columns in the matrix G, such that the sum of each of the columns in the normalized matrix Gn is equal to 1. A calculation procedure is then performed on the matrix Gn to execute the sentences ranking. In one embodiment, ranking of each sentence of the document 200 is represented by a vector {right arrow over (π)} that satisfies:
x is the number of the sentences and p is damping factor. For example, p is set to 0.85. Accordingly, the vector {right arrow over (π)} representing the ranking is the eigenvector of matrix
and corresponds to eigenvalue 1. As discussed previously, the targeted sentence(s) can be found by the afore-described random walk. In this regard, the calculation procedure involving the equation above is equivalent to implementing the random walk, and thus the vector {right arrow over (π)}, which is a solution for the equation, is equivalent to a result obtained from the random walk. That is, finding the vector {right arrow over (π)} means the sentence ranking is determined.
The electronic embodiments disclosed herein may be implemented using computing devices, computer processors, or electronic circuitries including but not limited to application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), and other programmable logic devices configured or programmed according to the teachings of the present disclosure. Computer instructions or software codes running in the computing devices, computer processors, or programmable logic devices can readily be prepared by practitioners skilled in the software or electronic art based on the teachings of the present disclosure.
All or portions of the electronic embodiments may be executed in one or more computing devices including server computers, personal computers, laptop computers, mobile computing devices such as smartphones and tablet computers.
The electronic embodiments include computer storage media having computer instructions or software codes stored therein which can be used to program computers or microprocessors to perform any of the processes of the present invention. The storage media can include, but are not limited to, floppy disks, optical discs, Blu-ray Disc, DVD, CD-ROMs, and magneto-optical disks, ROMs, RAMs, flash memory devices, or any type of media or devices suitable for storing instructions, codes, and/or data.
Various embodiments of the present invention also may be implemented in distributed computing environments and/or Cloud computing environments, wherein the whole or portions of machine instructions are executed in distributed fashion by one or more processing devices interconnected by a communication network, such as an intranet, Wide Area Network (WAN), Local Area Network (LAN), the Internet, and other forms of data transmission medium.
The foregoing description of the present invention has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations will be apparent to the practitioner skilled in the art.
The embodiments were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling others skilled in the art to understand the invention for various embodiments and with various modifications that are suited to the particular use contemplated.
