Preliminary Study of a New Approach to NMF Based Text Summarization Fused with Anaphora Resolution

Recently Non-negative Matrix Factorization (NMF) has captured a quantifiable attention in the field of information retrieval. Especially in the dimension of text summarization, it has leaped a large step in minimizing the gap between the summary produced by the man and machine. This paper is aimed at a preliminary study of a novel approach to automatic text summarization using extraction method by fusing the output of an anaphoric resolver with NMF. This initial study is aimed at studying the impact of changes in the weights of H matrix of NMF matrices by treating anaphoric pronoun replacements in the term by sentence matrix of the original document. It is done to emphasize how anaphoric resolutions can improve the performance of the output when the original term by document matrix is properly treated with anaphoric replacements using NMF method. Results of the preliminary study demonstrate that when the output of anaphoric resolver is fused with NMF better results can be achieved in automatic text summarization.