Extractive Single Document Summarization (SDS) is the task of summarizing a single document via extracting importance sentences verbatim and arranging them in a cohesive manner. It is different from Multi-Document Summarization (MDS) where multiple source documents are processed to generate a single summary. This paper proposes a two-stage mechanism to perform single document summarization via multi-document summarization technique. The approach involves the use of popular extractive summarization algorithms to generate summaries which are then further processed as multi-document summarization instance. The MDS approach used is based on word graph based sentence fusion followed by concept-based Integer Linear Programming (ILP) method for maximizing the coverage in sentence selection. The proposed system outperforms each of the single document summarizers by at least 2.6 percent point ROUGE scores, thereby indicating that performing single document summarization via multi-document summarization is a promising venue for further research in summarization.

Text Summarization, Single Document Summarization (SDS), Multi-Document Summarization (MDS), Extractive Summarization, Integer Linear Programming (ILP)

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