We investigate whether time features help to improve event-based summarization. In mis paper, events are defined as event terms and the associated event elements. While event terms represent the actions themselves, event elements denote action arguments. After anchoring events on the time line, two different statistical measures are employed to identify importance of events on each day. Experiments show that the combination of tfidf weighting scheme and time features can improve the quality of summaries significantly. The improvement can be attributed to its capability to represent the trend of news topics depending on event temporal distributions.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||8th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2007|
|Period||18/02/07 → 24/02/07|
- Theoretical Computer Science
- Computer Science(all)