An approach to automatic summarization for Chinese text based on the combination of spectral clustering and LexRank

In the past half century, automatic summarization has been a hot topic in the field of natural language processing, and it will be paid more and more attention to with the rapid development of the mobile network technology. Most of the automatic summarization research today is on extractive summarization, which mainly ranks the sentences according to their simple heuristic features such as the frequency of words they contain, their position in the text or paragraph and so on. Inspired by the great performance of LexRank, we manage to introduce LexRank to Chinese texts. In order to make up the deficiency of LexRank, spectral clustering is adopted to process the component analysis. All in all, we propose an approach of extractive summarization for Chinese text based on the combination of spectral clustering and LexRank, which is of high coverage and low redundancy. It is demonstrated by experiments that our approach has been greatly improved compared to the original LexRank. In addition, our approach is easy to implement and robust to noise.