We consider the problem of producing a multi-document summary given a collection of documents. Since most successful methods of multi-document summarization are still largely extractive, in this paper, we explore just how well an extractive method can perform. We introduce an "oracle" score, based on the probability distribution of unigrams in human summaries. We then demonstrate that with the oracle score, we can generate extracts which score, on average, better than the human summaries, when evaluated with ROUGE. In addition, we introduce an approximation to the oracle score which produces a system with the best known performance for the 2005 Document Understanding Conference (DUC) evaluation.
Other than these, topic-focused multi-document summarization using an approximate oracle score has been proposed by REF based on the probability distribution of unigrams in human summaries.
Topic-Focused Multi-Document Summarization Using An Approximate Oracle Score
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics - Poster Sessions
Computer Science