Analog Textual Entailment and Spectral Clustering (ATESC) Based Summarization

In the domain of single document and automatic extractive text summarization, a recent approach Logical TextTiling (LTT) has been proposed [1]. In-depth analysis has revealed that LTTs performance is limited by unfair entailment calculation, weak segmentation and assignment of equal importance to each segment produced. It seems that because of these drawbacks, the summary produced from experimentation on articles collected from New York Times website has been of poor/inferior quality. To overcome these limitations, the present paper proposes a novel technique called ATESC(Analog Textual Entailment and Spectral Clustering) which employs the use of analog entailment values in the range [0,1], segmentation using Normalized Spectral Clustering, and assignment of relative importance to the produced segments based on the scores of constituent sentences. At the end, a comparative study of results of LTT and ATESC is carried out. It shows that ATESC produces better quality of summaries in most of the cases tested experimentally.