Event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences describe. In this paper, we define an event as one or more event terms along with the named entities associated. Each event often relates to other events semantically, temporally, spatially, causally or conditionally. Firstly, we derive event relevance from external linguistic resource. Then we apply PageRank ranking algorithm to estimate the significance of an event for inclusion in a summary based on the event semantic relevance derived. We make experiments on the DUC 2001 test data only using the event semantic relevance, from external linguistic resource like VerbOcean, and the results make more improvement than those based on the tf*idf.
['Maofu Liu', 'Wenjie Li', 'Mingli Wu', 'Huijun Hu']
Event-Based Extractive Summarization Using Event Semantic Relevance from External Linguistic Resource