Skip to main content

Document details - Text summarization on Amazon food reviews

Journal Volume 9, Issue 2, March - April 2020, Article 9462141 Prof. Kalpana Deorukhkar, Deljin Jaison, Sanjeev Hippurgikar, Shubham Ambilkar , " Text summarization on Amazon food reviews" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 9, Issue 2, March - April 2020 , pp. 010-012 , ISSN 2278 - 6856.

Text summarization on Amazon food reviews

    Prof. Kalpana Deorukhkar, Deljin Jaison, Sanjeev Hippurgikar, Shubham Ambilkar

Abstract

Abstract- Every day, people rely on a wide variety of sources to stay informed from news stories to social media posts to search results. Being able to develop a model that can automatically deliver accurate summaries of longer text can be useful for digesting such large amounts of information in a compressed form. In this project, we make use of a deep learning model to tackle text summarization which involves generating a short summary for a longer piece of text. AText Summarization is broadly classified into mainly two types: extractive summarization and abstractive summarization.Extractive summarization is an approach in which the user selects passages from the source texts which are in long passage form and then arranges it to form a summary. Abstractive summarization means an abstractive approach which involves understanding the intent and writes the summary in its own words which are small and concise. This paper focuses on LSTM with attention-based encoder to solve the challenge of abstractive summarization on food reviews data and evaluated their effectiveness using ROUGE, BLEU score. Keywords— Text summarization, RNN, LSTM, Attention mechanism.

  • ISSN: 22786856
  • Source Type: Journal
  • Original language: English

Cited by 0 documents

Related documents

{"topic":{"name":"Order Picking; AS/RS; Warehouses","id":5729,"uri":"Topic/5729","prominencePercentile":98.30173,"prominencePercentileString":"98.302","overallScholarlyOutput":0},"dig":"7972b85ca5bc948c1a2f0423f8150b186ec6bb8cf32afac11c4a324b8d78fb11"}