Unsupervised document summarization from data reconstruction perspective

Due to its wide applications in information retrieval, document summarization is attracting increasing attention in natural language processing. A large body of recent literature has implemented document summarization by extracting sentences that cover the main topics of a document with a minimum redundancy. In this paper, we take a different perspective from data reconstruction and propose a novel unsupervised framework named Document Summarization based on Data Reconstruction (DSDR). Specifically, our approach generates a summary which consist of those sentences that can best reconstruct the original document. To model the relationship among sentences, we firstly introduce the linear reconstruction which approximates the document by linear combinations of the selected sentences. We then extend it into the non-negative reconstruction which allows only additive, not subtractive, linear combinations. In order to handle the nonlinear cases and respect the geometrical structure of sentence space, we also extend the linear reconstruction in the manifold adaptive kernel space which incorporates the manifold structure by using graph Laplacian. Extensive experiments on summarization benchmark data sets demonstrate that our proposed framework outperform state of the art.