Gibberish, Assistant, or Master? Using Tweets Linking to News for Extractive Single-Document Summarization
Zhongyu Wei, Wei Gao · WORLD SCIENTIFIC eBooks · 2017
Single-document summarization is a challenging task. In this paper, we explore effective ways using the tweets link-ing to news for generating extractive summary of each doc-ument. 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 summa-rization 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 af-ter 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.