Sequential Summarization: A Full View of Twitter Trending Topics
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)(2014)
摘要
As an information delivering platform, Twitter collects millions of tweets every day. However, some users, especially new users, often find it difficult to understand trending topics in Twitter when confronting the overwhelming and unorganized tweets. Existing work has attempted to provide a short snippet to explain a topic, but this only provides limited benefits and cannot satisfy the users' expectations. In this paper, we propose a new summarization task, namely sequential summarization, which aims to provide a serial of chronologically ordered short sub-summaries for a trending topic in order to provide a complete story about the development of the topic while retaining the order of information presentation. Different from the traditional summarization task, the numbers of sub-summaries for different topics are not fixed. Two approaches, i.e., stream-based and semantic-based approaches, are developed to detect the important subtopics within a trending topic. Then a short sub-summary is generated for each subtopic. In addition, we propose three new measures to evaluate the position-aware coverage, sequential novelty and sequence correlation of the system-generated summaries. The experimental results based on the proposed evaluation criteria have demonstrated the effectiveness of the proposed approaches.
更多查看译文
关键词
overwhelming tweets,information delivering platform,system-generated summaries,new summarization task,snippet,chronologically-ordered short sub-summaries,semantic-based approach,traditional summarization task,evaluation criteria,summarization task,twitter trending topics,new measure,full view,short sub-summary,position-aware coverage,sequential novelty,sequence correlation,user expectation satisfaction,sequential summarization,stream-based approach,different topic,subtopic detection,social networking (online),information presentation,unorganized tweets,short sub-summaries,new user,short snippet
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络