Multi-document Summarization Using Minimum Distortion

Abstract

Document summarization plays an important role in the area of natural language processing and text mining. This paper proposes several novel information-theoretic models for multi-document summarization. They consider document summarization as a transmission system and assume that the best summary should have the minimum distortion. By defining a proper distortion measure and a new representation method, the combination of the last two models (the linear representation model and the facility location model) gains good experimental results on the DUC2002 and DUC2004 datasets. Moreover, we also indicate that the model has high interpretability and extensibility.

An Optimization Approach to Automatic Generic Document Summarization

“…In particular, they formulate four objective functions, namely information coverage, significance, redundancy, and text coherence. Authors of work (Ma and Wan 2010) propose three new models based on the optimization of an information theoretic measure: distortion. The p-median model respects the optimization as a p-median problem and conveys as more information between the whole summary and whole original documents as possible.…”

The combination of term relations analysis and weighted frequent itemset model for multidocument summarization

“…In some studies, search engines are used to generate extractive summarization . Extractive and abstractive summarizations are two categories used for summarizing documents . In extractive summarization, the important sentences are selected to generate a summary, whereas in abstractive summarization, important contents are discovered and reformulated to produce a document summary.…”