Ian Jung  Su Jeong Choi  Seyoung Park

10.5626/JOK.2019.46.11.1140

news stream summarization multi-document summarization timeline generation Extractive Summarization timeline summarization

This paper explores the summarization task in news stream, as it is continuously produced and has sequential characteristic. Timeline based summarization is widely adopted in news stream summarization because timeline can represent events sequentially. However, previous work relies on the time of collection of news article, thus they cannot consider for dates other than out of the collected period. In addition, previous work lacked consideration of conciseness, informativeness, and coherence. To address these problems, we propose a news stream summarization model with an expanded timeline. The model takes into consideration the expanded timeline by using time points that are referenced in given news articles and selects sentences that are concise, informative and consistent with neighboring time points. First, we constitute expanded timeline by selecting dates which are from all identified time points in the news articles. Then, we extract sentences as summary with consideration of informativeness based on keyword for each time points, and on coherence between two consecutive time points, and on continuity of named entities except for long sentence in the articles. Experimental results show that the proposed model generated higher quality summaries compared to previous work.