Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2015

Abstract

Single-document summarization is a challenging task. In this paper, we explore effective ways using the tweets linking to news for generating extractive summary of each document. We reveal the very basic value of tweets that can be utilized by regarding every tweet as a vote for candidate sentences. Base on such finding, we resort to unsupervised summarization models by leveraging the linking tweets to master the ranking of candidate extracts via random walk on a heterogeneous graph. The advantage is that we can use the linking tweets to opportunistically "supervise" the summarization with no need of reference summaries. Furthermore, we analyze the influence of the volume and latency of tweets on the quality of output summaries since tweets come after news release. Compared to truly supervised summarizer unaware of tweets, our method achieves significantly better results with reasonably small tradeoff on latency; compared to the same using tweets as auxiliary features, our method is comparable while needing less tweets and much shorter time to achieve significant outperformance.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 38th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

First Page

1003

Last Page

1006

Identifier

10.1145/2766462.2767835

Publisher

ACM Press

City or Country

Santiago, Chile

Additional URL

https://doi.org/10.1145/2766462.2767835

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