A Regression-Based Approach Using Integer Linear Programming for Single-Document Summarization

Most of the existing approaches for extractive singledocument summarization of news articles rely on a single method to summarize all input documents. Recent work demonstrated that this is a significant limitation, since no summarization technique can achieve high performance for all input articles. In this context, this paper proposes a new regression-based approach using Integer Linear Programming (ILP) for single-document summarization. The proposed solution relies on a concept-based ILP method to generate multiple candidate summaries for each input article exploring different concept weighting methods and representation forms. Afterward, a regression model enriched with several extracted features at summary, sentence and ngram level is applied to select among the candidates the most informative summary based on an estimation of the traditional ROUGE-1 score. The investigated features are derived from indicators of content importance such as frequency, position, and coverage. Experiments conducted on the DUC 2001-2002 and CNN corpora show that the proposed method statistically outperforms other state-of-the-art extractive summarization approaches in most scenarios regarding ROUGE-1 and ROUGE-2 recall measures.