Multi-document summarization via group sparse learning

• A novel compressive sensing based multi-document summarization with group sparse learning (SGS) framework is proposed.
• Sentences in documents are considered as a kind of sparse or compressible signals.
• We jointly select summary sentences with the learnt group structure pattern to discriminate the important and the redundant information simultaneously.
• An accelerated projection gradient algorithm is developed to solve the group sparse convex optimization problem of the proposed framework efficiently.
• Experimental results on DUC 2006 and TAC 2007 main task corpus demonstrate the effectiveness of our proposed framework.

Multi-document summarization (MDS) aims to capture the core information from a set of topic-specific documents. Most existing extractive methods evaluate sentences individually and select summary sentences one by one, which may ignore the hidden structure patterns among sentences and fail to keep less redundancy from the global perspective. We study this task from the perspective of compressive sensing, consider sentences in documents as a kind of signals, which are usually sparse or compressible in the sense that they have concise representation pattern expressed in the proper sentence basis and transform it to a group sparse representation issue. A novel multi-document Summarization with Group Sparse learning (SGS) framework is proposed, which can maximally reconstruct the original documents via minimizing the approximation error and jointly select summary sentences with the learnt group structure information among sentences. The summary relatedness can be modeled by constraining the reconstruction models to be close to each other, and make multiple sentences share a common underlying structure to form the summary content. With this model, we take the global information into account in evaluating the importance of sentences and further reduce the redundancy. In order to solve this group sparse convex optimization problem for MDS, we also develop an efficient algorithm based on the Nesterov’s method, which leads to much faster convergence rate than some traditional methods. Experimental results on DUC 2006 and TAC 2007 main task corpora show the effectiveness of our proposed framework. The relevant experiments are conducted to demonstrate the working mechanism of main components in the SGS framework.