Search (18 results, page 1 of 1)

  • × theme_ss:"Automatisches Abstracting"
  1. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.02 
    0.024432644 = product of:
  0.09773058 = sum of:
    0.09773058 = sum of:
      0.047271654 = weight(_text_:access in 6599) [ClassicSimilarity], result of:
        0.047271654 = score(doc=6599,freq=2.0), product of:
          0.15778996 = queryWeight, product of:
            3.389428 = idf(docFreq=4053, maxDocs=44218)
            0.046553567 = queryNorm
          0.29958594 = fieldWeight in 6599, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.389428 = idf(docFreq=4053, maxDocs=44218)
            0.0625 = fieldNorm(doc=6599)
      0.050458927 = weight(_text_:22 in 6599) [ClassicSimilarity], result of:
        0.050458927 = score(doc=6599,freq=2.0), product of:
          0.16302267 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046553567 = queryNorm
          0.30952093 = fieldWeight in 6599, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0625 = fieldNorm(doc=6599)
  0.25 = coord(1/4)
    Abstract
    With the onset of the information explosion arising from digital libraries and access to a wealth of information through the Internet, the need to efficiently determine the relevance of a document becomes even more urgent. Describes a text extraction system (TES), which retrieves a set of sentences from a document to form an indicative abstract. Such an automated process enables information to be filtered more quickly. Discusses the combination of various text extraction techniques. Compares results with manually produced abstracts
    Date
    26. 2.1997 10:22:43
  2. Pinto, M.: Abstracting/abstract adaptation to digital environments : research trends (2003) 0.02 
    0.015645592 = product of:
  0.062582366 = sum of:
    0.062582366 = weight(_text_:open in 4446) [ClassicSimilarity], result of:
      0.062582366 = score(doc=4446,freq=2.0), product of:
        0.20964009 = queryWeight, product of:
          4.5032015 = idf(docFreq=1330, maxDocs=44218)
          0.046553567 = queryNorm
        0.2985229 = fieldWeight in 4446, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.5032015 = idf(docFreq=1330, maxDocs=44218)
          0.046875 = fieldNorm(doc=4446)
  0.25 = coord(1/4)
    Abstract
    The technological revolution is affecting the structure, form and content of documents, reducing the effectiveness of traditional abstracts that, to some extent, are inadequate to the new documentary conditions. Aims to show the directions in which abstracting/abstracts can evolve to achieve the necessary adequacy in the new digital environments. Three researching trends are proposed: theoretical, methodological and pragmatic. Theoretically, there are some needs for expanding the document concept, reengineering abstracting and designing interdisciplinary models. Methodologically, the trend is toward the structuring, automating and qualifying of the abstracts. Pragmatically, abstracts networking, combined with alternative and complementary models, open a new and promising horizon. Automating, structuring and qualifying abstracting/abstract offer some short-term prospects for progress. Concludes that reengineering, networking and visualising would be middle-term fruitful areas of research toward the full adequacy of abstracting in the new electronic age.
  3. Hobson, S.P.; Dorr, B.J.; Monz, C.; Schwartz, R.: Task-based evaluation of text summarization using Relevance Prediction (2007) 0.02 
    0.015645592 = product of:
  0.062582366 = sum of:
    0.062582366 = weight(_text_:open in 938) [ClassicSimilarity], result of:
      0.062582366 = score(doc=938,freq=2.0), product of:
        0.20964009 = queryWeight, product of:
          4.5032015 = idf(docFreq=1330, maxDocs=44218)
          0.046553567 = queryNorm
        0.2985229 = fieldWeight in 938, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.5032015 = idf(docFreq=1330, maxDocs=44218)
          0.046875 = fieldNorm(doc=938)
  0.25 = coord(1/4)
    Abstract
    This article introduces a new task-based evaluation measure called Relevance Prediction that is a more intuitive measure of an individual's performance on a real-world task than interannotator agreement. Relevance Prediction parallels what a user does in the real world task of browsing a set of documents using standard search tools, i.e., the user judges relevance based on a short summary and then that same user - not an independent user - decides whether to open (and judge) the corresponding document. This measure is shown to be a more reliable measure of task performance than LDC Agreement, a current gold-standard based measure used in the summarization evaluation community. Our goal is to provide a stable framework within which developers of new automatic measures may make stronger statistical statements about the effectiveness of their measures in predicting summary usefulness. We demonstrate - as a proof-of-concept methodology for automatic metric developers - that a current automatic evaluation measure has a better correlation with Relevance Prediction than with LDC Agreement and that the significance level for detected differences is higher for the former than for the latter.
  4. Dorr, B.J.; Gaasterland, T.: Exploiting aspectual features and connecting words for summarization-inspired temporal-relation extraction (2007) 0.02 
    0.015645592 = product of:
  0.062582366 = sum of:
    0.062582366 = weight(_text_:open in 950) [ClassicSimilarity], result of:
      0.062582366 = score(doc=950,freq=2.0), product of:
        0.20964009 = queryWeight, product of:
          4.5032015 = idf(docFreq=1330, maxDocs=44218)
          0.046553567 = queryNorm
        0.2985229 = fieldWeight in 950, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.5032015 = idf(docFreq=1330, maxDocs=44218)
          0.046875 = fieldNorm(doc=950)
  0.25 = coord(1/4)
    Abstract
    This paper presents a model that incorporates contemporary theories of tense and aspect and develops a new framework for extracting temporal relations between two sentence-internal events, given their tense, aspect, and a temporal connecting word relating the two events. A linguistic constraint on event combination has been implemented to detect incorrect parser analyses and potentially apply syntactic reanalysis or semantic reinterpretation - in preparation for subsequent processing for multi-document summarization. An important contribution of this work is the extension of two different existing theoretical frameworks - Hornstein's 1990 theory of tense analysis and Allen's 1984 theory on event ordering - and the combination of both into a unified system for representing and constraining combinations of different event types (points, closed intervals, and open-ended intervals). We show that our theoretical results have been verified in a large-scale corpus analysis. The framework is designed to inform a temporally motivated sentence-ordering module in an implemented multi-document summarization system.
  5. Uyttendaele, C.; Moens, M.-F.; Dumortier, J.: SALOMON: automatic abstracting of legal cases for effective access to court decisions (1998) 0.01 
    0.0073119607 = product of:
  0.029247843 = sum of:
    0.029247843 = product of:
      0.058495685 = sum of:
        0.058495685 = weight(_text_:access in 495) [ClassicSimilarity], result of:
          0.058495685 = score(doc=495,freq=4.0), product of:
            0.15778996 = queryWeight, product of:
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046553567 = queryNorm
            0.3707187 = fieldWeight in 495, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.0546875 = fieldNorm(doc=495)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    The SALOMON project summarises Belgian criminal cases in order to improve access to the large number of existing and future cases. A double methodology was used when developing SALOMON: the cases are processed by employing additional knowledge to interpret structural patterns and features on the one hand and by way of occurrence statistics of index terms on the other. SALOMON performs an initial categorisation and structuring of the cases and subsequently extracts the most relevant text units of the alleged offences and of the opinion of the court. The SALOMON techniques do not themselves solve any legal questions, but they do guide the use effectively towards relevant texts
  6. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.01 
    0.006307366 = product of:
  0.025229463 = sum of:
    0.025229463 = product of:
      0.050458927 = sum of:
        0.050458927 = weight(_text_:22 in 6751) [ClassicSimilarity], result of:
          0.050458927 = score(doc=6751,freq=2.0), product of:
            0.16302267 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046553567 = queryNorm
            0.30952093 = fieldWeight in 6751, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=6751)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    6. 3.1997 16:22:15
  7. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.01 
    0.006307366 = product of:
  0.025229463 = sum of:
    0.025229463 = product of:
      0.050458927 = sum of:
        0.050458927 = weight(_text_:22 in 6974) [ClassicSimilarity], result of:
          0.050458927 = score(doc=6974,freq=2.0), product of:
            0.16302267 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046553567 = queryNorm
            0.30952093 = fieldWeight in 6974, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=6974)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  8. Plaza, L.; Stevenson, M.; Díaz, A.: Resolving ambiguity in biomedical text to improve summarization (2012) 0.01 
    0.0051703374 = product of:
  0.02068135 = sum of:
    0.02068135 = product of:
      0.0413627 = sum of:
        0.0413627 = weight(_text_:access in 2734) [ClassicSimilarity], result of:
          0.0413627 = score(doc=2734,freq=2.0), product of:
            0.15778996 = queryWeight, product of:
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046553567 = queryNorm
            0.2621377 = fieldWeight in 2734, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2734)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    Access to the vast body of research literature that is now available on biomedicine and related fields can be improved with automatic summarization. This paper describes a summarization system for the biomedical domain that represents documents as graphs formed from concepts and relations in the UMLS Metathesaurus. This system has to deal with the ambiguities that occur in biomedical documents. We describe a variety of strategies that make use of MetaMap and Word Sense Disambiguation (WSD) to accurately map biomedical documents onto UMLS Metathesaurus concepts. Evaluation is carried out using a collection of 150 biomedical scientific articles from the BioMed Central corpus. We find that using WSD improves the quality of the summaries generated.
  9. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.00 
    0.0047305245 = product of:
  0.018922098 = sum of:
    0.018922098 = product of:
      0.037844196 = sum of:
        0.037844196 = weight(_text_:22 in 948) [ClassicSimilarity], result of:
          0.037844196 = score(doc=948,freq=2.0), product of:
            0.16302267 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046553567 = queryNorm
            0.23214069 = fieldWeight in 948, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=948)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.
  10. Moens, M.-F.; Uyttendaele, C.: Automatic text structuring and categorization as a first step in summarizing legal cases (1997) 0.00 
    0.0044317176 = product of:
  0.01772687 = sum of:
    0.01772687 = product of:
      0.03545374 = sum of:
        0.03545374 = weight(_text_:access in 2256) [ClassicSimilarity], result of:
          0.03545374 = score(doc=2256,freq=2.0), product of:
            0.15778996 = queryWeight, product of:
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046553567 = queryNorm
            0.22468945 = fieldWeight in 2256, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046875 = fieldNorm(doc=2256)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    The SALOMON system automatically summarizes Belgian criminal cases in order to improve access to the large number of existing and future court decisions. SALOMON extracts relevant text units from the case text to form a case summary. Such a case profile facilitates the rapid determination of the relevance of the case or may be employed in text search. In a first important abstracting step SALOMON performs an initial categorization of legal criminal cases and structures the case text into separate legally relevant and irrelevant components. A text grammar represented as a semantic network is used to automatically determine the category of the case and its components. Extracts from the case general data and identifies text portions relevant for further abstracting. Prior knowledge of the text structure and its indicative cues may support automatic abstracting. A text grammar is a promising form for representing the knowledge involved
  11. Moens, M.-F.; Uyttendaele, C.; Dumotier, J.: Abstracting of legal cases : the potential of clustering based on the selection of representative objects (1999) 0.00 
    0.0044317176 = product of:
  0.01772687 = sum of:
    0.01772687 = product of:
      0.03545374 = sum of:
        0.03545374 = weight(_text_:access in 2944) [ClassicSimilarity], result of:
          0.03545374 = score(doc=2944,freq=2.0), product of:
            0.15778996 = queryWeight, product of:
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046553567 = queryNorm
            0.22468945 = fieldWeight in 2944, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046875 = fieldNorm(doc=2944)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    The SALOMON project automatically summarizes Belgian criminal cases in order to improve access to the large number of existing and future court decisions. SALOMON extracts text units from the case text to form a case summary. Such a case summary facilitates the rapid determination of the relevance of the case or may be employed in text search. an important part of the research concerns the development of techniques for automatic recognition of representative text paragraphs (or sentences) in texts of unrestricted domains. these techniques are employed to eliminate redundant material in the case texts, and to identify informative text paragraphs which are relevant to include in the case summary. An evaluation of a test set of 700 criminal cases demonstrates that the algorithms have an application potential for automatic indexing, abstracting, and text linkage
  12. Wang, W.; Hwang, D.: Abstraction Assistant : an automatic text abstraction system (2010) 0.00 
    0.0044317176 = product of:
  0.01772687 = sum of:
    0.01772687 = product of:
      0.03545374 = sum of:
        0.03545374 = weight(_text_:access in 3981) [ClassicSimilarity], result of:
          0.03545374 = score(doc=3981,freq=2.0), product of:
            0.15778996 = queryWeight, product of:
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046553567 = queryNorm
            0.22468945 = fieldWeight in 3981, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046875 = fieldNorm(doc=3981)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    In the interest of standardization and quality assurance, it is desirable for authors and staff of access services to follow the American National Standards Institute (ANSI) guidelines in preparing abstracts. Using the statistical approach an extraction system (the Abstraction Assistant) was developed to generate informative abstracts to meet the ANSI guidelines for structural content elements. The system performance is evaluated by comparing the system-generated abstracts with the author's original abstracts and the manually enhanced system abstracts on three criteria: balance (satisfaction of the ANSI standards), fluency (text coherence), and understandability (clarity). The results suggest that it is possible to use the system output directly without manual modification, but there are issues that need to be addressed in further studies to make the system a better tool.
  13. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.00 
    0.0039421036 = product of:
  0.015768414 = sum of:
    0.015768414 = product of:
      0.03153683 = sum of:
        0.03153683 = weight(_text_:22 in 5290) [ClassicSimilarity], result of:
          0.03153683 = score(doc=5290,freq=2.0), product of:
            0.16302267 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046553567 = queryNorm
            0.19345059 = fieldWeight in 5290, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5290)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 7.2006 17:25:48
  14. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.00 
    0.0039421036 = product of:
  0.015768414 = sum of:
    0.015768414 = product of:
      0.03153683 = sum of:
        0.03153683 = weight(_text_:22 in 2640) [ClassicSimilarity], result of:
          0.03153683 = score(doc=2640,freq=2.0), product of:
            0.16302267 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046553567 = queryNorm
            0.19345059 = fieldWeight in 2640, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2640)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 1.2016 12:29:41
  15. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.00 
    0.0039421036 = product of:
  0.015768414 = sum of:
    0.015768414 = product of:
      0.03153683 = sum of:
        0.03153683 = weight(_text_:22 in 889) [ClassicSimilarity], result of:
          0.03153683 = score(doc=889,freq=2.0), product of:
            0.16302267 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046553567 = queryNorm
            0.19345059 = fieldWeight in 889, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=889)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 1.2023 18:57:12
  16. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00 
    0.0039421036 = product of:
  0.015768414 = sum of:
    0.015768414 = product of:
      0.03153683 = sum of:
        0.03153683 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
          0.03153683 = score(doc=1012,freq=2.0), product of:
            0.16302267 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046553567 = queryNorm
            0.19345059 = fieldWeight in 1012, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1012)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 6.2023 14:55:20
  17. Sweeney, S.; Crestani, F.; Losada, D.E.: 'Show me more' : incremental length summarisation using novelty detection (2008) 0.00 
    0.003693098 = product of:
  0.014772392 = sum of:
    0.014772392 = product of:
      0.029544784 = sum of:
        0.029544784 = weight(_text_:access in 2054) [ClassicSimilarity], result of:
          0.029544784 = score(doc=2054,freq=2.0), product of:
            0.15778996 = queryWeight, product of:
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046553567 = queryNorm
            0.18724121 = fieldWeight in 2054, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2054)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    The paper presents a study investigating the effects of incorporating novelty detection in automatic text summarisation. Condensing a textual document, automatic text summarisation can reduce the need to refer to the source document. It also offers a means to deliver device-friendly content when accessing information in non-traditional environments. An effective method of summarisation could be to produce a summary that includes only novel information. However, a consequence of focusing exclusively on novel parts may result in a loss of context, which may have an impact on the correct interpretation of the summary, with respect to the source document. In this study we compare two strategies to produce summaries that incorporate novelty in different ways: a constant length summary, which contains only novel sentences, and an incremental summary, containing additional sentences that provide context. The aim is to establish whether a summary that contains only novel sentences provides sufficient basis to determine relevance of a document, or if indeed we need to include additional sentences to provide context. Findings from the study seem to suggest that there is only a minimal difference in performance for the tasks we set our users and that the presence of contextual information is not so important. However, for the case of mobile information access, a summary that contains only novel information does offer benefits, given bandwidth constraints.
  18. Dunlavy, D.M.; O'Leary, D.P.; Conroy, J.M.; Schlesinger, J.D.: QCS: A system for querying, clustering and summarizing documents (2007) 0.00 
    0.0029544784 = product of:
  0.0118179135 = sum of:
    0.0118179135 = product of:
      0.023635827 = sum of:
        0.023635827 = weight(_text_:access in 947) [ClassicSimilarity], result of:
          0.023635827 = score(doc=947,freq=2.0), product of:
            0.15778996 = queryWeight, product of:
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.046553567 = queryNorm
            0.14979297 = fieldWeight in 947, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.389428 = idf(docFreq=4053, maxDocs=44218)
              0.03125 = fieldNorm(doc=947)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel integrated information retrieval system-the Query, Cluster, Summarize (QCS) system-which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of methods in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test sets from the Document Understanding Conferences (DUC) as measured by the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence "trimming" and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.

Authors

Years