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        <title>Using Relevant Public Posts to Enhance News Article Summarization</title>
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    <abstract>A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article. In this paper we explore using public posts following a new article to improve automatic summary generation for the news article. We propose different approaches to incorporate information from public posts, including using frequency information from the posts to re-estimate bigram weights in the ILP-based summarization model and to re-weight a dependency tree edge’s importance for sentence compression, directly selecting sentences from posts as the final summary, and finally a strategy to combine the summarization results generated from news articles and posts. Our experiments on data collected from Facebook show that relevant public posts provide useful information and can be effectively leveraged to improve news article summarization results.</abstract>
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%0 Conference Proceedings
%T Using Relevant Public Posts to Enhance News Article Summarization
%A Li, Chen
%A Wei, Zhongyu
%A Liu, Yang
%A Jin, Yang
%A Huang, Fei
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F li-etal-2016-using
%X A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article. In this paper we explore using public posts following a new article to improve automatic summary generation for the news article. We propose different approaches to incorporate information from public posts, including using frequency information from the posts to re-estimate bigram weights in the ILP-based summarization model and to re-weight a dependency tree edge’s importance for sentence compression, directly selecting sentences from posts as the final summary, and finally a strategy to combine the summarization results generated from news articles and posts. Our experiments on data collected from Facebook show that relevant public posts provide useful information and can be effectively leveraged to improve news article summarization results.
%U https://aclanthology.org/C16-1054
%P 557-566

Markdown (Informal)

[Using Relevant Public Posts to Enhance News Article Summarization](https://aclanthology.org/C16-1054) (Li et al., COLING 2016)

• Using Relevant Public Posts to Enhance News Article Summarization (Li et al., COLING 2016)

ACL

• Chen Li, Zhongyu Wei, Yang Liu, Yang Jin, and Fei Huang. 2016. Using Relevant Public Posts to Enhance News Article Summarization. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 557–566, Osaka, Japan. The COLING 2016 Organizing Committee.