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Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.


Papers
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Book ChapterDOI
23 Oct 2016
TL;DR: The amount of redundancy in a document collection can be used for assigning importance to sentences in multi-document extractive summarization and an unsupervised graph-based technique is proposed that allows for experiment with intra-document and inter-document redundancy.
Abstract: Multi-document summarization differs from single-document summarization in excessive redundancy of mentions of some events or ideas. We show how the amount of redundancy in a document collection can be used for assigning importance to sentences in multi-document extractive summarization: for instance, an idea could be important if it is redundant across documents because of its popularity; on the other hand, an idea could be important if it is not redundant across documents because of its novelty. We propose an unsupervised graph-based technique that, based on proper similarity measures, allows us to experiment with intra-document and inter-document redundancy. Our experiments on DUC corpora show promising results.

2 citations

Proceedings Article
26 Apr 2004
TL;DR: This article discusses a technique of generating hierarchical topic trees of a text and to use them in various ways to build summaries of a flexible length and compares the results when the topic tree is used for automatic summarization.
Abstract: Summarizing document texts at various levels of detail is required for many information selection tasks For instance, when loading and visualizing documents on small screens of handheld devices, it is important to be able to dynamically compress texts In this article we discuss a technique of generating hierarchical topic trees of a text and to use them in various ways to build summaries of a flexible length For the topic tree building process we have implemented both a deterministic and probabilistic approach We compare the results when the topic tree is used for automatic summarization

2 citations

Book ChapterDOI
13 Dec 2004
TL;DR: A multi-document summarizer in Chinese, ACRUX, which contains three new techniques: a fuzzy classification method based on KNN (FAMKNN), Subject-Oriented Multi- document Summarization (SOMS), and Multi-document Summarizing with Link Analysis.
Abstract: This paper describes a multi-document summarizer in Chinese, ACRUX, which contains three new techniques: a fuzzy classification method based on KNN (FAMKNN), Subject-Oriented Multi-document Summarization (SOMS), and Multi-document Summarization with Link Analysis.

2 citations

Proceedings Article
01 Jan 2005
TL;DR: An approach for summarization from multiple documents which report on events that evolve through time, taking into account the different document sources, is presented, distinguish the evolution of an event into linear and non-linear.
Abstract: Panagiotis Stamatopoulos Department of Informatics, University of Athens, Greece takis@di.uoa.gr Abstract We present an approach for summarization from multiple documents which report on events that evolve through time, taking into account the different document sources. We distinguish the evolution of an event into linear and non-linear. According to our approach, each document is represented by a collection of messages which are then used in order to instantiate the cross-document relations that determine the summary content. The paper presents the summarization system that implements this approach through a case study on linear evolution.

2 citations

Book ChapterDOI
20 Jun 2011
TL;DR: This paper proposes an ontology enhanced multimedia summarization environment able to derive a synthetic representation of audio/video contents by a limited loss of meaningful information while overcoming the information overload problem.
Abstract: The growing amount of multimedia data acquired during courtroom debates makes information and knowledge management in judicial domain a real challenge. In this paper we tackle the problem of summarizing this large amount of multimedia data in order to support fast navigation of the streams, efficient access to the information and effective representation of relevant contents needed during the judicial process. In particular, we propose an ontology enhanced multimedia summarization environment able to derive a synthetic representation of audio/video contents by a limited loss of meaningful information while overcoming the information overload problem.

2 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202374
2022160
202152
202061
201947
201852