Most approaches to extractive summarization define a set of features upon which selection of sentences is based, using algorithms independent of the features themselves. We propose a new set of features based on low-level, atomic events that describe relationships between important actors in a document or set of documents. We investigate the effect this new feature has on extractive summarization, compared with a baseline feature set consisting of the words in the input documents, and with state-of-the-art summarization systems. Our experimental results indicate that not only the event-based features offer an improvement in summary quality over words as features, but that this effect is more pronounced for more sophisticated summarization methods that avoid redundancy in the output.
Filatova and Hatzivassiloglou REF proposed a set of event-based features which not only offers an improvement in summary quality over words as features (based on tf-idf scores), but also avoids redundancies in event summaries.
Event-Based Extractive Summarization
Workshop On Text Summarization Branches Out
Computer Science