-
Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009)
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- Abstract
- Sentence ranking is the issue of most concern in document summarization today. While traditional feature-based approaches evaluate sentence significance and rank the sentences relying on the features that are particularly designed to characterize the different aspects of the individual sentences, the newly emerging graph-based ranking algorithms (such as the PageRank-like algorithms) recursively compute sentence significance using the global information in a text graph that links sentences together. In general, the existing PageRank-like algorithms can model well the phenomena that a sentence is important if it is linked by many other important sentences. Or they are capable of modeling the mutual reinforcement among the sentences in the text graph. However, when dealing with multidocument summarization these algorithms often assemble a set of documents into one large file. The document dimension is totally ignored. In this article we present a framework to model the two-level mutual reinforcement among sentences as well as documents. Under this framework we design and develop a novel ranking algorithm such that the document reinforcement is taken into account in the process of sentence ranking. The convergence issue is examined. We also explore an interesting and important property of the proposed algorithm. When evaluated on the DUC 2005 and 2006 query-oriented multidocument summarization datasets, significant results are achieved.
-
Ouyang, Y.; Li, W.; Li, S.; Lu, Q.: Intertopic information mining for query-based summarization (2010)
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- Abstract
- In this article, the authors address the problem of sentence ranking in summarization. Although most existing summarization approaches are concerned with the information embodied in a particular topic (including a set of documents and an associated query) for sentence ranking, they propose a novel ranking approach that incorporates intertopic information mining. Intertopic information, in contrast to intratopic information, is able to reveal pairwise topic relationships and thus can be considered as the bridge across different topics. In this article, the intertopic information is used for transferring word importance learned from known topics to unknown topics under a learning-based summarization framework. To mine this information, the authors model the topic relationship by clustering all the words in both known and unknown topics according to various kinds of word conceptual labels, which indicate the roles of the words in the topic. Based on the mined relationships, we develop a probabilistic model using manually generated summaries provided for known topics to predict ranking scores for sentences in unknown topics. A series of experiments have been conducted on the Document Understanding Conference (DUC) 2006 data set. The evaluation results show that intertopic information is indeed effective for sentence ranking and the resultant summarization system performs comparably well to the best-performing DUC participating systems on the same data set.
-
Jones, S.; Paynter, G.W.: Automatic extractionof document keyphrases for use in digital libraries : evaluations and applications (2002)
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- Abstract
- This article describes an evaluation of the Kea automatic keyphrase extraction algorithm. Document keyphrases are conventionally used as concise descriptors of document content, and are increasingly used in novel ways, including document clustering, searching and browsing interfaces, and retrieval engines. However, it is costly and time consuming to manually assign keyphrases to documents, motivating the development of tools that automatically perform this function. Previous studies have evaluated Kea's performance by measuring its ability to identify author keywords and keyphrases, but this methodology has a number of well-known limitations. The results presented in this article are based on evaluations by human assessors of the quality and appropriateness of Kea keyphrases. The results indicate that, in general, Kea produces keyphrases that are rated positively by human assessors. However, typical Kea settings can degrade performance, particularly those relating to keyphrase length and domain specificity. We found that for some settings, Kea's performance is better than that of similar systems, and that Kea's ranking of extracted keyphrases is effective. We also determined that author-specified keyphrases appear to exhibit an inherent ranking, and that they are rated highly and therefore suitable for use in training and evaluation of automatic keyphrasing systems.
-
Xiong, S.; Ji, D.: Query-focused multi-document summarization using hypergraph-based ranking (2016)
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-
Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014)
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- Abstract
- Automatic text summarization has been an active field of research for many years. Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms. The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization - they can be used to understand the input text in terms of salient concepts from the knowledge base. In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking. Our approach is to first construct a bipartite sentence-concept graph, and then rank the input sentences using iterative updates on this graph. We consider several models for the bipartite graph, and derive convergence properties under each model. Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively. Finally, we present a Wikipedia-based multi-document summarization algorithm. An important feature of the proposed algorithms is that they enable real-time incremental summarization - users can first view an initial summary, and then request additional content if interested. We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality. We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles.
-
Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016)
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- Date
- 22. 1.2016 12:29:41
-
Pinto, M.: Engineering the production of meta-information : the abstracting concern (2003)
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- Date
- 27.11.2005 18:29:55
- Source
- Journal of information science. 29(2003) no.5, S.405-418
-
Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994)
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- Date
- 16. 8.1998 12:30:29
-
Craven, T.C.: ¬A phrase flipper for the assistance of writers of abstracts and other text (1995)
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- Date
- 17. 8.1996 10:29:59
-
Goh, A.; Hui, S.C.: TES: a text extraction system (1996)
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- Date
- 26. 2.1997 10:22:43
-
Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996)
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- Date
- 6. 3.1997 16:22:15
-
Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996)
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- 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
-
Uyttendaele, C.; Moens, M.-F.; Dumortier, J.: SALOMON: automatic abstracting of legal cases for effective access to court decisions (1998)
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- Date
- 17. 7.1996 14:16:29
-
Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007)
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- 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.
-
Sweeney, S.; Crestani, F.; Losada, D.E.: 'Show me more' : incremental length summarisation using novelty detection (2008)
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- Date
- 29. 7.2008 19:35:12
-
Wang, S.; Koopman, R.: Embed first, then predict (2019)
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- Date
- 29. 9.2019 12:18:42
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Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006)
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- Date
- 22. 7.2006 17:25:48
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Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023)
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- Date
- 22. 1.2023 18:57:12
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Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023)
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- Date
- 22. 6.2023 14:55:20
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Kannan, R.; Ghinea, G.; Swaminathan, S.: What do you wish to see? : A summarization system for movies based on user preferences (2015)
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- Date
- 25. 1.2016 18:45:29
