Utilizing Microblogs for Automatic News Highlights Extraction

Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 872–883, Dublin, Ireland, August 23-29 2014. Utilizing Microblogs for Automatic News Highlights Extraction Zhongyu Wei The Chinese University of Hong Kong Shatin, N.T. Hong Kong zywei@se.cuhk.edu.hk Wei Gao Qatar Computing Research Institute Qatar Foundation Daha, Qatar wgao@qf.org.qa Abstract Story highlights form a succinct single-document summary consisting of 3-4 highlight sentences that reflect the gist of a news article. Automatically producing news highlights is very challeng- ing. We propose a novel method to improve news highlights extraction by using microblogs. The hypothesis is that microblog posts, although noisy, are not only indicative of important pieces of information in the news story, but also inherently “short and sweet” resulting from the artificial compression effect due to the length limit. Given a news article, we formulate the problem as two rank-then-extract tasks: (1) we find a set of indicative tweets and use them to assist the ranking of news sentences for extraction; (2) we extract top ranked tweets as a substitute of sentence ex- traction. Results based on our news-tweets pairing corpus indicate that the method significantly outperform some strong baselines for single-document summarization. 1 Introduction People in this era are overloaded by their daily exposure to large amount of online information. To make life easier, some news websites like CNN.com and USAToday.com provide “Story Highlights” in their news articles for readers to get the gist of story quickly. The highlights of an article typically contain 3-4 summary sentences in bullet-points form that are representative of and shorter than the original new sentences in the article. An example of story highlights of an article is shown in Figure 1 (marked in red rectangle) that are written in a compact, almost telegraphic style. In contrast to the original content of the article, significant compression is obtained by shortening and paraphrasing. Unfortunately, the production of such good-quality highlights needs to be done manually which is very expensive. Existing methods face grand technical challenges for automating the process. The task is complex in nature due to a broad range of linguistic constraints which ultimately requires wide-coverage of language understanding beyond the capabilities of current NLP technology (Woodsend and Lapata, 2010). Most automatic systems simplify the problem using extractive approach. By using linguistic or statistical information or both, the key units or concepts can be identified from sentences or across multiple documents, and then the sentences are scored and extracted according to their informativeness with the presence of the key components. The extractive approach has two salient problems: (1) it is commonly ineffective to locate key sen- tences, meaning that the presence of linguistically and/or statistically important units does not necessarily indicate a highlight sentence. This is evidenced by the fact that sophisticated systems for Document Un- derstanding Conference (DUC) summarization task cannot significantly outperform a trivial baseline that simply selects first n sentences of the document (Nenkova, 2005); (2) sentence extracts as highlights are extraordinarily verbose in general, which need to be …

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