2020
AACL
AACL 2020
Hindi History Note Generation with Unsupervised Extractive Summarization
Abstract
AbstractIn 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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Conference Pioneer
— AACL 2020
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Keyword Pioneer
— text rank
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Cross-Pollinator
— Artificial Intelligence, Deep Learning, Knowledge & Reasoning, Machine Learning, Natural Language Processing, Reinforcement Learning