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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Proceedings Article•
06 Jun 2010TL;DR: It is provided evidence that intrinsic evaluation of summaries using Amazon's Mechanical Turk is quite difficult and that non-expert judges are not able to recover system rankings derived from experts.
Abstract: We provide evidence that intrinsic evaluation of summaries using Amazon's Mechanical Turk is quite difficult. Experiments mirroring evaluation at the Text Analysis Conference's summarization track show that non-expert judges are not able to recover system rankings derived from experts.
87 citations
18 Mar 2001
TL;DR: A system for finding, visualizing and summarizing a topic-based cluster of news stories and producing summaries of a subset of the stories that it finds, according to parameters specified by the user.
Abstract: NEWSINESSENCE is a system for finding, visualizing and summarizing a topic-based cluster of news stories. In the generic scenario for NEWSINESSENCE, a user selects a single news story from a news Web site. Our system then searches other live sources of news for other stories related to the same event and produces summaries of a subset of the stories that it finds, according to parameters specified by the user.
86 citations
TL;DR: This work proposes an approach that uses statistical tools to improve content selection in multi-document automatic text summarization, and the results are promising when compared with some existing techniques.
Abstract: This work proposes an approach that uses statistical tools to improve content selection in multi-document automatic text summarization. The method uses a trainable summarizer, which takes into account several features: the similarity of words among sentences, the similarity of words among paragraphs, the text format, cue-phrases, a score related to the frequency of terms in the whole document, the title, sentence location and the occurrence of non-essential information. The effect of each of these sentence features on the summarization task is investigated. These features are then used in combination to construct text summarizer models based on a maximum entropy model, a naive-Bayes classifier, and a support vector machine. To produce the final summary, the three models are combined into a hybrid model that ranks the sentences in order of importance. The performance of this new method has been tested using the DUC 2002 data corpus. The effectiveness of this technique is measured using the ROUGE score, and the results are promising when compared with some existing techniques.
85 citations
Proceedings Article•
01 Jan 2006
TL;DR: Computational approaches to summarizing dynamically introduced information: online discussions and blogs, and their evaluations are described; when branching into these newly emerged data types, there are number of difficulties that are discussed here.
Abstract: In this paper we describe computational approaches to summarizing dynamically introduced information: online discussions and blogs, and their evaluations. Research in the past has been mainly focused on text-based summarization where the input data is predominantly newswire data. When branching into these newly emerged data types, we face number of difficulties that are discussed here.
84 citations