Automatic text summarization by local scoring and ranking for improving coherence

Existence of large amount of textual information available on the internet emerged serious research in the area of machine generated summarization. Manual summarization of these online text documents is a very difficult task for human beings. So we need an automatic text summarizer. Automatic Text Summarization (ATS) is “condensing the source text into a shorter version, while preserving its information content and overall meaning”. Even though the work of automatic text summarization started in 1950's, still it is lacking to achieve more coherent and meaningful summaries. The proposed approach provides automatic feature based extractive heading wise text summarizer to improve the coherence thereby improving the understandability of the summary text. It summarizes the given input document using local scoring and local ranking that is it provides heading wise summary. Headings of a document give contextual information and permit visual scanning of the document to find the search contents. The proposed approach applies the same features to all document sentences. But it ranks the sentences heading wise and selects top n sentences from each heading where n depends upon compression ratio. The final heading wise summary produced by this approach is a collection of summary of individual headings. Since the heading wise summary contains the equal proportion of sentences from each heading, it reduces the coherent gap of the summary text. Also it improves the overall meaning and understanding of the summary text. The outcomes of the experiment clearly show that heading wise summarizer provides better precision, recall and f-measure over the main summarizer, Ms-word summarizer, free summarizer and Auto summarizer.