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 chapter, 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.
|Title of host publication||Social Media Content Analysis|
|Subtitle of host publication||Natural Language Processing and Beyond|
|Publisher||World Scientific Publishing Co. Pte. Ltd.|
|Number of pages||25|
|Publication status||Published - 1 Jan 2017|
ASJC Scopus subject areas
- Computer Science(all)