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Article

TGSum: build tweet guided multi-document summarization dataset

Authors:

Ming ZhouAuthors Info & Claims

AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence

Pages 2906 - 2912

Published: 12 February 2016 Publication History

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.

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Cited By

Chowdhury TChakraborty TDey LChaudhury SKrishnapuram RSingla PRoy R(2019)CQASUMMProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3297001.3297004(18-26)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1145/3297001.3297004
Nguyen MTran DNguyen LPhan X(2018)Exploiting User Posts for Web Document SummarizationACM Transactions on Knowledge Discovery from Data10.1145/318656612:4(1-28)Online publication date: 8-Jun-2018
https://dl.acm.org/doi/10.1145/3186566
Yao JWan XXiao J(2017)Recent advances in document summarizationKnowledge and Information Systems10.1007/s10115-017-1042-453:2(297-336)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s10115-017-1042-4

TGSum: build tweet guided multi-document summarization dataset
1. Computing methodologies
  1. Artificial intelligence

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AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence

February 2016

4406 pages

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Association for the Advancement of Artificial Intelligence

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AAAI Press

Publication History

Published: 12 February 2016

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Cited By

Chowdhury TChakraborty TDey LChaudhury SKrishnapuram RSingla PRoy R(2019)CQASUMMProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3297001.3297004(18-26)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1145/3297001.3297004
Nguyen MTran DNguyen LPhan X(2018)Exploiting User Posts for Web Document SummarizationACM Transactions on Knowledge Discovery from Data10.1145/318656612:4(1-28)Online publication date: 8-Jun-2018
https://dl.acm.org/doi/10.1145/3186566
Yao JWan XXiao J(2017)Recent advances in document summarizationKnowledge and Information Systems10.1007/s10115-017-1042-453:2(297-336)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s10115-017-1042-4

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