SUMTOD: SUMMARIZATION OF TODDLER STORIES THROUGH SENTENCE REDUCTION AND SENTENCE COMBINATION
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Abstract
Natural language processing (NLP) is a field of computer science and linguistics, concerned with the interactions between computers and human languages. It processes the data through lexical analysis, syntax analysis, semantic analysis, discourse processing, pragmatic analysis. Sentence compression or text summarization is one of the most thought-provoking tasks in Natural language processing. This paper presents an approach to summarize toddler story (SUMTOD) using Sentence Reduction and Sentence Combination. The algorithm in this paper first finds the theme of the story. Then it applies the human summarization rules. After this it applies the approach of sentence similarity either to remove or retain the sentences. The algorithm has been tested on various documents of English as well as for compressing the toddler stories too. The intrinsic evaluation and extrinsic evaluation is performed through ROUGE tool and human experts respectively. The algorithm has attained the compression ratio as 39%. The slighter value for compression ratio signifies the superior results.
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