MSCSO: Extractive Multi-document Summarization Based on a New Criterion of Sentences Overlapping

Abstract

Extractive multi-document summarization receives a set of documents and extracts the important sentences to form a summary. This paper proposes a novel multi-document summarization with sentences overlapping. First, we preprocess multi-document and calculate 12 features of each sentence. This paper suggests four new features: ROUGE-1 and ROUGE-2 score between the sentence and a single document, ROUGE-1 and ROUGE-2 score between the sentence and multiple documents, also a new definition of sentence overlapping feature. Then, we assign each sentence a score by the learned model. We calculate pairwise overlapping between the sentences and finally select the sentences with higher score and less redundancy. These sentences are given to form the final summary to output under a length constraint. Our method is language free, and it can be implemented on other languages with minor changes. The proposed method is tested on DUC 2006 and 2007 datasets. The effectiveness of this technique is measured using the ROUGE score, and the results are promising when they have been compared with some existing methods.