Topic
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.
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TL;DR: A set of 14 summarizers are developed, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores to select sentences for an extractive summary of texts.
139 citations
Proceedings Article•
01 May 2014
TL;DR: A corpus of summaries produced by several state-of-the-art extractive summarization systems or by popular baseline systems is presented to facilitate future research on generic summarization and motivates the need for development of more sensitive evaluation measures and for approaches to system combination in summarization.
Abstract: In the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed. Intuitive simple approaches have also been shown to perform unexpectedly well for the task. Yet it is practically impossible to compare the existing approaches directly, because systems have been evaluated on different datasets, with different evaluation measures, against different sets of comparison systems. Here we present a corpus of summaries produced by several state-of-the-art extractive summarization systems or by popular baseline systems. The inputs come from the 2004 DUC evaluation, the latest year in which generic summarization was addressed in a shared task. We use the same settings for ROUGE automatic evaluation to compare the systems directly and analyze the statistical significance of the differences in performance. We show that in terms of average scores the state-of-the-art systems appear similar but that in fact they produce very different summaries. Our corpus will facilitate future research on generic summarization and motivates the need for development of more sensitive evaluation measures and for approaches to system combination in summarization.
138 citations
TL;DR: A news delivery and summarization system, acting as a user's agent, gathers and recaps news items based on specifications and interests.
Abstract: A news delivery and summarization system, acting as a user's agent, gathers and recaps news items based on specifications and interests.
135 citations
TL;DR: Different LSA-based summarization algorithms are explained, two of which are proposed by the authors of this paper and their performances are compared using their ROUGE scores.
Abstract: Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to creating well-formed summaries. One of the newest methods is the Latent Semantic Analysis (LSA). In this paper, different LSA-based summarization algorithms are explained, two of which are proposed by the authors of this paper. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores. One of our algorithms produces the best scores and both algorithms perform equally well on Turkish and English document sets.
133 citations