Tweet Summarization of News Articles: An Objective Ordering-Based Perspective

Abstract

Twitter has become an essential platform for the news media sources to disseminate news. The opinions expressed through Twitter can be mined by news media sources to obtain users’ reactions centered around different news articles. A comprehensive summary of the users’ reactions with respect to a news article can be crucial due to various reasons like: 1) understanding the sensitivity/importance of the news; 2) obtaining insights about the diverse opinions of the readers with respect to the news; and 3) understanding the key aspects that draw the interest of the readers. However, the selected summary tweets must fulfill multiple objectives, like relevance to the news article, diversity among the selected tweets, and should cover the entire spectrum of opinions expressed through the tweets. Existing methods primarily attempt to identify a set of relevant tweets from which the summary tweets are selected that maintains the diversity and coverage requirements. However, the noise and the nontemporal behavior of the article-specific tweets make the identification of such relevant tweets extremely difficult, resulting in poor summary quality. In this paper, through empirical investigations, we show that initially identifying the diverse opinions can lead to better identification of the relevant tweets, i.e., following a specific ordering of the objectives can lead to the improved summary. We, subsequently, propose a tweet summarization technique that follows such a specific ordering. Validation of our proposed approach for 800 news articles with 2.1 billion related tweets shows that the proposed approach produces 11.6%–34.8% improvement in summary quality as compared to existing state-of-the-art techniques.