An Approach for Combining Semantic Information and Proximity Information for Text Summarization

Abstract

This paper develops and evaluates an approach for combining semantic information with proximity information for text summarization. The approach is based on the proximity language model, which incorporates proximity information into the unigram language model. This paper novelly expands the proximity language model to also incorporate semantic information using latent semantic analysis (LSA). We argue that this approach achieves a good balance between syntactic and semantic information. We evaluate the approach using ROUGE scores on the Text Analysis Conference (TAC) 2009 Summarization task, and find that incorporating LSA into PLM gives improvements over the baseline models. 3 PROXIMITY LANGUAGE MODEL Proximity language model (PLM) forms the heart of our ranking function, and is based on the unigram language model (Zhao and Yun, 2009).