Abstract:
A system for genre-specific summarization of documents is provided that overcomes the problem of summarizing heterogeneous document collections by taking the genre, or type, of document into account when selecting summary sentences. The system of the present invention takes advantage of the structure and wording of various document genres to provide faster and more accurate summaries.

Description:
FIELD OF THE INVENTION 
     The present invention relates to the field of document management, and more particularly, to a system for summarizing documents that uses information about a document&#39;s genre, or document type, for selecting summary sentences for an automatically generated summary. 
     BACKGROUND 
     A user faced with a huge document or a collection of documents typically wants to obtain a summary of the documents in order to save time or to answer a specific question. The task of summarizing a document involves finding a small number of sentences that provide a concise characterization of the document. Existing approaches for summarizing documents apply only one summarization strategy, thus ignoring variations in the structure and wording of different genres of documents. Some examples of different document genres include newspaper articles, editorials, reference manuals, scientific works and tutorials. One problem with existing approaches is they can be slow and inaccurate when applied to heterogeneous document collections. A heterogeneous document collection includes documents of different genres, or document types such as fiction, scientific or other non-fiction works, etc. 
     SUMMARY 
     The present invention provides a system for genre-specific summarization of documents. The system of the present invention overcomes the problem of summarizing heterogeneous document collections by taking the genre, or type, of document into account when selecting summary sentences. We have discovered that one problem with applying known document summarization techniques to heterogeneous collections is that the assumptions made by such techniques may not apply across the population of the collection. Such assumptions include where in a document sentences which contain summary information might be located, keywords which may indicate summary information, etc. By taking genre into account, the system of the present invention takes advantage of the structure and wording of various document genres to provide faster and more accurate summaries. For example, document genres such as newspaper articles tend to have good summary sentences in the beginning and document genres such as research papers tend to have good summary sentences in the conclusion. The system of the present invention takes this information into account when selecting summary sentences. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     A more complete appreciation of the invention and many of its attendant advantages will be readily obtained and understood by referring to the following detailed description and the accompanying drawings in which like reference numerals denote like elements as between the various drawings. The drawings are briefly described below. 
     FIG. 1 is a flowchart illustrating steps that are performed in a method for summarizing documents in an embodiment of the present invention. 
     FIG. 2 is a flowchart illustrating steps that are performed in a method for applying a genre-specific summarizer in another embodiment of the present invention. 
     FIG. 3 is a flowchart illustrating steps that are performed in a method for applying a genre-specific summarizer in yet another embodiment of the present invention. 
     FIG. 4 is a flowchart illustrating a continuation from the flowchart of FIG.  3 . 
     FIG. 5 is a flowchart illustrating steps that are performed in a method for summarizing documents in an embodiment of the present invention employing a feature vector. 
    
    
     DETAILED DESCRIPTION 
     An embodiment of the present invention provides a system for summarizing documents that takes advantage of genre-specific characteristics of documents and collections of heterogeneous documents. The system of the present invention performs methods that may be implemented on a computer system having a computer-readable medium and may be performed using computer-executable instructions. The computer-executable instructions may be included in a computer program product. The methods may also include transferring a computer program product from one or more first computers to one or more second computers through a communications medium. 
     FIG. 1 is a flowchart  100  illustrating steps that are performed in an example of a method for summarizing documents in an embodiment of the present invention. The method begins, step  102 , and the document genre may be determined, step  106 . Some examples of document genres include newspaper articles (reportage), editorials, reference manuals, scientific works, technical articles, legal documents, nonfiction, fiction and tutorials. The document genre may be predetermined or determined at the time of summarization. The genre of a document may be determined by observable properties called facets. Some examples of document facets include communication mode (narrative, directive, suasive, descriptive), audience (broadcast, directed), brow (high, medium, low) and authorship (institutional, personal). For a more detailed discussion of facets and genre detection, see Kessler et al.,  Automatic Detection of Text Genre , available on ftp://parcftp.xerox.com/pub/qca/papers/genre, which is herein incorporated by reference. Table 1 shows some examples of how document genres might be identified based on various facets. The genre characteristics of Table 1 are included by way of example only, and are not meant to place any limits on how document genres may be defined and/or identified. 
     
       
         
               
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                   
                   
                   
                 Communication 
                   
               
               
                 Genre 
                 Audience 
                 Authorship 
                 Mode 
                 Brow 
               
               
                   
               
             
             
               
                 Sci/Tech 
                 directed 
                 institutional 
                 descriptive, 
                 medium-high 
               
               
                   
                   
                   
                 narrative 
               
               
                 News 
                 broadcast 
                 institutional 
                 descriptive, 
                 low-high 
               
               
                   
                   
                   
                 narrative 
               
               
                 Novel 
                   
                 personal 
                 narrative 
                 low-high 
               
               
                 Editorial 
                 directed 
                 personal 
                 suasive 
                 medium-high 
               
               
                 Legal 
                 directed 
                 personal 
                 suasive 
                 medium-high 
               
               
                 Manual 
                 directed 
                 institutional 
                 directive 
                 medium 
               
               
                   
               
             
          
         
       
     
     A determination may then be made as to whether a summarization routine is available for the particular document genre, step  108 . If none is available, then the method could optionally flag an error to make the user aware that there is no summarization routine available, step  110 , or the method could simply end, step  124 . 
     If a summarization routine is available for the document genre identified in step  106 , then that summarization routine might be loaded, step  112 . The document may then be split up into sentences, step  114 . A sentence may be received, step  116 , and analyzed by a genre-specific summarization routine, step  118 , by processes that are described further below. The genre-specific summarization routine analyzes the sentence and assigns a score to the sentence, indicating its usefulness as a summary sentence. The assigned score may depend on features of the genre. The probable location of summary sentences is an example of a genre feature. For example, newspaper articles tend to contain summary sentences at the beginning of the document, but technical articles tend to contain summary sentences at the end of the document, or following key phrases such as the words “Conclusion” or “Summary”. Genre features are used for characterizing sentences within a document to determine whether or not they are useful for summarizing the document. 
     Different classifiers may be used for summarizing different document genres. A decision tree as described in, for example, Manning et al., “Foundations of Statistical Natural Language Processing” or Mitchell, “Machine Learning”, which are herein incorporated by reference, might be more useful for reportage such as newspaper articles, but a probabilistic classifier, for example as described in the above references might be more useful for fiction. 
     An example of a document summarization routine is described in more detail below in the discussion of FIGS. 2-4. If there are more sentences available, step  120 , then the next sentence may be received, step  116 , and processed by the genre-specific summarization routine, step  118 , until all of the sentences in the document have been analyzed and scored. After all of the sentences in the document have been analyzed, the sentences with the assigned scores that are the most consistent with a set of criteria that indicate which sentences make good summary sentences, may be selected as summary sentences, step  122 , and processing may end, step  124 . In an example of an embodiment of the present invention, sentences that are most likely to be summary sentences may be weighted more heavily than other sentences, and may receive higher assigned scores. (Lower assigned scores might also be used for indicating good summary sentences, depending on how the system for rating sentences as summary sentences is implemented.) The sentences having the highest assigned scores are then selected as summary sentences. A cut-off threshold for the assigned score may be selected to limit the number of sentences that are selected as summary sentences. For example, a user may select a criterion such as selecting the n highest-scoring sentences. The value of n may be a fixed value, e.g. n=5 or n may be variable. Such a threshold could be determined for example, by the genre of the document, by the length of the sentence, or by the position of the sentence within a paragraph or within the document. Other factors may also be considered in determining a cut-off threshold. The threshold may be preset or may be dynamically determined by, for example, the computer system. Also, sentences that are the most likely to be summary sentences may be weighted lower than other sentences, and receive lower assigned scores. 
     FIG. 2 is a flowchart  200  illustrating an example of steps that may be performed in a method for applying a genre-specific summarizer in an embodiment of the present invention. The method of flowchart  200  begins, step  202 , during processing of the method shown in flowchart  100  in FIG. 1, after step  116 , where a sentence may be received. A summary score may be assigned to the sentence being processed based on, for example, the sentence length, step  240 . Other criteria may also be applied to apply summary scores to a sentence, for example the average word length of a sentence. A summary score might then be assigned to the sentence based on the position of the paragraph containing the sentence being processed within the document or set of documents being analyzed or summarized, step  242 . A summary score might also be assigned based on the sentence position within in a paragraph, step  244 . Other summary scores may also be assigned at this point, such as for use of keywords, etc. 
     Continuous representations of the input may also be used to determine summary scores for each sentence. For example, the first sentence in a paragraph containing five sentences might contain a paragraph position score of ⅕=0.2. Similarly, the third sentence in a paragraph containing eight sentences might be assigned a paragraph position score of ⅜=0.375. The scores from the evaluations of sentence length, average word length per sentence, sentence position within a paragraph, etc. are later combined to produce an overall score for the sentence. From the overall scores for each sentence, a composite score might be created for each sentence and the sentences having the highest composite score are selected in step  122 , described above in FIG.  1 . The above scoring method is given by way of example, but it should be noted that any scoring method could be used. 
     For additional information on scoring methods that may be used, see Kupiec et al., U.S. Pat. No. 5,778,397, “Automatic Method of Generating Feature Probabilities for Automatic Extracting Summarization”; Kupiec et al., U.S. Pat. No. 5,918,240, “Automatic Method of Extracting Summarization Using Feature Probabilities”; Chen et al., U.S. Pat. No. 5,745,602, “Automatic Method of Selecting Multi-Word Key Phrases from a Document”; Chen, U.S. Pat. No. 5,689,716 “Automatic Method of Generating Thematic Summaries”; and Kupiec et al., “A Trainable Document Summarizer”, Proceedings of SIGIR 95, pp. 68-73, 1995, which are herein incorporated by reference. 
     FIG. 3 is a flowchart  300  illustrating an example of steps that may be performed in a method for applying a genre-specific summarizer in an embodiment of the present invention. The method of flowchart  300  might begin, step  202 , during processing of the method shown in flowchart  100  in FIG. 1, after step  116 , where a sentence might be received. Steps  204 - 210 , indicated by dotted-line block  240 , assign a summary score to the sentence being processed based, for example, on the sentence length. The length of the sentence is determined, step  204 , and may be compared to a threshold T, step  206 . Any appropriate threshold may be used, but it is preferably a value indicating a threshold length that provides information as to whether the sentence is more likely or less likely to be a summary sentence. This threshold length may be predetermined and input, obtained from another source, determined dynamically, etc. A length factor may be assigned based on the comparison of the length of the sentence to the threshold. If the length factor is greater than the threshold, then a summary score may be assigned based on a sentence that is longer, or greater in length, than the threshold in step  208 . Processing may then continue at step  212 , described below. If the length factor is less than or equal to the threshold, then a summary score may be assigned based on a sentence that is shorter than or the same length as the threshold in step  210 . Processing may then continue at step  212 , described below. 
     Steps  212 - 222 , indicated by dotted-line block  242 , assign a summary score based on the position of the paragraph containing the sentence being processed within the document or set of documents being analyzed or summarized. A paragraph position factor may be assigned, indicating where the paragraph lies within the document or set of documents. The paragraph position factor may be compared to a threshold, and depending on the result of the comparison, the sentence may be scored appropriately. In an example, if the paragraph falls within the first five paragraphs of the document in step  214 , it is processed as an initial paragraph, as in step  216 . If the paragraph falls within the last five paragraphs of the document in step  218 , then it may be processed as an ending paragraph, as in step  220 . Otherwise, the paragraph may be assumed to lie somewhere between the first five paragraphs and the last five paragraphs of the document and may be assigned a score reflecting that position in step  222 . Processing may continue, as shown by continuation indicator A, step  224 , from FIG. 3 to FIG.  4 . 
     FIG. 4 is a continuation of flowchart  400  from FIG. 3, illustrating steps that may be performed in a method for summarizing documents in an embodiment of the present invention. Steps  226 - 236 , indicated by dotted-line block  244 , assign a summary score based on where the sentence being processed falls within a paragraph. A sentence position factor may be assigned, indicating where the sentence lies in the paragraph. The sentence position factor may be compared to a threshold, and depending on the result of the comparison, the sentence may be scored appropriately. The position of a sentence within the paragraph may be determined, step  226 , and then a summary score may be assigned based on the position of the sentence within the paragraph. If the sentence is near the beginning of the paragraph, step  228 , then a summary score may be assigned based on that determination in step  230  and processing may end at step  238 . If the sentence is near the end of the paragraph, step  232 , then a summary score may be assigned based on that determination in step  234  and processing may end at step  238 . Otherwise, the sentence may be assumed to be in or near the middle of the paragraph, i.e., not in an initial position at the beginning of the paragraph, or in a final position at the end of the paragraph. If the sentence is in a medial position, somewhere in or near the middle of the paragraph, then a summary score may be assigned based on that determination in step  236 . Processing may then end at step  238 . 
     It should be noted that the group of steps  204 - 210  (block  240 ), the group of steps  212 - 222  (block  242 ), and the grouping of steps  226 - 236  (block  244 ) might be performed in any order relative to each other, or with intervening steps. Also, depending on how the document genre is characterized in terms of which sentences make good summary sentences, it may not be necessary to process all of the groups of steps to obtain an assigned score. 
     FIG. 5 is a flowchart  500  illustrating steps that may be performed in a method for summarizing documents in an embodiment of the present invention. In the embodiment shown by flowchart  500 , instead of using a genre-specific summarizer, a feature vector may be created for each sentence. The feature vector may take characteristics of genre into account but does not require that the genre be predetermined. This embodiment is useful for documents or collections of documents for which the genre is not known or determinable prior to applying the summarizer. The method begins, step  502 , and at least one document may be received, step  504 . The document may be split up into sentences, step  506 , and each sentence may be analyzed, steps  508 - 513 . A sentence may be received, step  508 , and a feature vector may be constructed, step  510 , as will be described further below. 
     A single feature vector may contain information on the standard summarization features of the sentence. An example of a feature vector for a sentence is as follows. Assume that a position value of 0.0 is assigned to a sentence or a paragraph having a position near the beginning, and that a position value of 1.0 is assigned to a sentence or paragraph near the end. For example, a value of 0.0 might be assigned to a sentence having a position at the beginning of a paragraph, or to a paragraph having a position at the beginning of a document. Similarly, a value of 1.0 might be assigned to a sentence having a position at the end of a paragraph, or to a paragraph having a position at the end of a document. A feature vector for a sentence might include the following values: position in paragraph is 0.375, indicating that the sentence is ⅜ away from the beginning of the paragraph; position of paragraph in document is 1.0, indicating that the paragraph is as the end of the document; and average word length is 7.8, indicating the average number of words in a sentence. (See score for Sentence  4  in Table 2 below.) 
     A single feature vector may also be concatenated with genre-identification features that pertain to the document as a whole in order to create a composite feature vector that may provide genre-specific summarization when used with any trainable classification method, described in more detail below. An example of the contents of a feature vector for a sentence (see sentence  5  in Table 2 below) with genre features concatenated is as follows: position in paragraph is 0.5; position of paragraph in document is 1.0; average word length is 7.8; average number of punctuation marks is 0.05; average number of latinate endings is 0.21; and average word length of document is 6.9. 
     Table 2 shows an example of how feature vectors might be defined. The first column indicates which sentence is being examined. The second, third and fourth columns indicate the scores for Feature  1 , Feature  2 , and Feature  3  respectively. Feature  1  is the position of a sentence within a paragraph, feature  2  is the position of the paragraph in the document, and feature  3  is the average word length for the sentence. More or less features may be included in a feature vector as appropriate. The last column (“Score”) indicates the total score when the three features values associated with a particular sentence are added up. (For example, the score for sentence n, referred to as Fn in Table 2 below, would be Fn=F 1 +F 2 +F 3 .) In an example embodiment of the present invention, a document summary may be obtained by selecting the sentences having the highest scores. The feature vectors described in Table 2 are included by way of example only, and are not meant to place any limits on feature vectors might be defined and/or identified. 
     
       
         
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 Sentence 
                 Feature 1 
                 Feature 2 
                 Feature 3 
                 Score 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1 
                 0.375 
                 0.0 
                 7.8 
                 8.175 
               
               
                 2 
                 .5 
                 0.3 
                 8.0 
                 8.8 
               
               
                 3 
                 1.0 
                 .5 
                 4.0 
                 6.0 
               
               
                 4 
                 0.375 
                 1.0 
                 7.8 
                 9.175 
               
               
                 5 
                 .5 
                 1.0 
                 7.8 
                 9.3 
               
               
                 n 
                 F1 
                 F2 
                 F3 
                 Fn 
               
               
                   
               
             
          
         
       
     
     A determination is made as to whether there are more sentences to process, step  512 . If there are more sentences, then processing may continue at step  508 . Otherwise, if there are no more sentences to process, processing may continue to step  514  where a trainable classification method may be applied to the feature vectors obtained in step  510  to determine which sentences are summary sentences. After classification, the method may end, step  516 . 
     Examples of trainable classification methods that may be used in step  514  include, but are not limited to, classification methods that take binary features or feature vectors as input and classification methods that take continuously valued features and vectors as input. Classification methods that take binary features or feature vectors include decision trees and Bayesian classifiers. For example, a decision tree in which splits on the genre features are interleaved with splits on the summarization features may be used in an embodiment of the present invention. A second-order Bayesian classifier in which the probability of a summary feature value may be conditioned on the value of one of the genre features may also be used. For more details on decision trees and other probabilistic classifiers, see Manning et al. and Mitchell, incorporated by reference above. 
     Classification methods that use continuous features may include, for example, logistic regressions, neural networks and other gradient descent learning algorithms. A classification method can be formalized as a function mapping from feature vectors (elements of R{circumflex over ( )}n) to probabilities. Note that classification methods based on binary features are a special case of classification methods based on continuous features. A binary distinction is between yes and no. This can be represented as two different numbers; for example 0.0 and 1.0, to indicate zero possibility vs. certainty, as described above in the above examples relating to feature vectors. However, any other pair of distinct numbers may be used to reflect a binary distinction, for example −1 and +1. 
     While the embodiments of the present invention described herein have focused on genre-specific summarization featuring specific examples of genre summarizing criteria and trainable classification methods, other summarizing criteria and trainable classification methods could be used without departing from the spirit and scope of the present invention. Thus, it should be appreciated that the above description is merely illustrative, and should not be read to limit the scope of the invention or the claims.