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.