Hindi History Note Generation with Unsupervised Extractive Summarization
In this work, the task of extractive single document summarization applied to an education setting to generate summaries of chapters from grade 10 Hindi history textbooks is undertaken. Unsupervised approaches to extract summaries are employed and evaluated. TextRank, LexRank, Luhn and KLSum are used to extract summaries. When evaluated intrinsically, Luhn and TextRank summaries have the highest ROUGE scores. When evaluated extrinsically, the effective measure of a summary in answering exam questions, TextRank summaries performs the best.
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Document SummarizationExtractive SummarizationUnsupervised Extractive SummarizationSimilar Papers 제목 키워드 기반
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