Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66236
Title: TGSum : build tweet guided multi-document summarization dataset
Authors: Cao, Z
Chen, C
Li, W
Li, S
Wei, F
Zhou, M
Issue Date: 2016
Publisher: AAAI press
Source: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, p. 2906-2912 How to cite?
Abstract: The development of summarization research has been significantly hampered by the costly acquisition of reference summaries. This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social media's reactions.We utilize two types of social labels in tweets, i.e., hashtags and hyper-links. Hashtags are used to cluster documents into different topic sets. Also, a tweet with a hyper-link often highlights certain key points of the corresponding document. We synthesize a linked document cluster to form a reference summary which can cover most key points. To this aim, we adopt the ROUGE metrics to measure the coverage ratio, and develop an Integer Linear Programming solution to discover the sentence set reaching the upper bound of ROUGE. Since we allow summary sentences to be selected from both documents and high-quality tweets, the generated reference summaries could be abstractive. Both informativeness and readability of the collected summaries are verified by manual judgment. In addition, we train a Support Vector Regression summarizer on DUC generic multi-document summarization benchmarks. With the collected data as extra training resource, the performance of the summarizer improves a lot on all the test sets. We release this dataset for further research.
Description: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, US, 12-17 February 2016
URI: http://hdl.handle.net/10397/66236
ISBN: 9781577357605
Appears in Collections:Conference Paper

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