Though both document summarization and keyword extraction aim to extract concise representations from documents, these two tasks have usually been investigated independently. This paper proposes a novel iterative reinforcement approach to simultaneously extracting summary and keywords from single document under the assumption that the summary and keywords of a document can be mutually boosted. The approach can naturally make full use of the reinforcement between sentences and keywords by fusing three kinds of relationships between sentences and words, either homogeneous or heterogeneous. Experimental results show the effectiveness of the proposed approach for both tasks. The corpus-based approach is validated to work almost as well as the knowledge-based approach for computing word semantics.
REF proposed a method that fuses three kinds of relationships between sentences and words: relationships between words, relationships between sentences, and relationships between words and sentences.
Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction
45th Annual Meeting of the Association of Computational Linguistics
Computer Science