In this paper, we present event-based summarization and investigate whether it can be enhanced by integrating temporal distribution information. We refer to events as event terms and the associated event elements. Event terms represent actions themselves and event elements are commonly those verb arguments. After anchoring events on the time line, we explore two statistical measures, i.e. tf*idf and x2, for evaluating the importance of events on each day. Summary sentences are selected based on the weights of the events contained in them, in either sequential or round robin order. Experiments show that the combination of time-based tf*idf weighting scheme and sequential sentence selection strategy can improve the quality of summaries significantly. The improvement can be attributed to its capability of representing the trend of news topics based on event temporal distributions.
|Number of pages||22|
|Journal||International journal of computer processing of languages|
|Publication status||Published - 2006|
- Text summarization
- Event-based summarization
- Event temporal distribution
- Sentence clustering