An interpretable method for text summarization based on simplicial non-negative matrix factorization

ABSTRACT

Automatic text summarization plays an important role in information retrieval and text mining. Furthermore, it provides an useful solution to the information overload problem. In this paper, we propose a simplicial NMF-based unsupervised generic document summarization method which can inherit some advantages of simplicial NMF such as easy interpretability, low complexity, convexity and sparsity. By focusing on the major topics contained within every sentence as well as entire document, our method generates better summaries with less repetition. The effectiveness of our method is proved by experimental results. On the summarization performance, our approach obtains mostly higher ROUGE scores than NMF-based method.

References

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Index Terms

• Information systems

  • Information retrieval