Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
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In this paper, we propose an event-based approach for Chinese sentence compression without using any training corpus. We enhance the linguistically-motivated heuristics by exploiting event word significance and event information density. This is shown to improve the preservation of important information and the tolerance of POS and parsing errors, which are more common in Chinese than English. The heuristics are only required to determine possibly removable constituents instead of selecting specific constituents for removal, and thus are easier to develop and port to other languages and domains. The experimental results show that around 72% of our automatic compressions are grammatically and semantically correct, preserving around 69% of the most important information on average.