The implementation of Text Summarization With Deep Learning Approach

The implementation of Text Summarization With Deep Learning Approach

Text summarization is the technique, which automatically creates an abstract or extractive summary of a text. Text summarization is one of the research works in NLP, which concentrates on providing meaningful summaries using various NLP tools and techniques. Abstractive and extractive summarizations are two methods of generating summaries from texts. This study has identified Text Summarization in Deep Learning” as a research topic. The primary purpose of the study is to design a system and implement extractive and abstractive proclamation text summarization to come up with an effective and efficient summarization type as well as to evaluate the extent of the fitness of the algorithms.  Accordingly, abstractive text summarization models (Sequence- 2-Sequence decoder with attention) and extractive text summarization models (TextRank) were developed for text summarization of the dataset. Different comparison measures (Rouge-1 and Rouge-2 percentage, count vectorizer, the vectorizer, and soft-cosine similarity) were implemented to evaluate the text summaries produced. Results of the Rouge-1 and Rouge-2 measurement percentage index were higher for abstractive summarization than that of the extractive one in this case. Besides the algorithms and models used for both summarization methods fit for the proclamation text summarizations.

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