Extractive Summarization using Deep Learning

August 15, 2017 · Entered Twilight · 🏛 Research on computing science

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Repo contents: .ipynb_checkpoints, README.md, Summarizer.py, articles, enhancedfeatureSum, entity2.py, entity2.pyc, featureSum, input-output.pdf, lengths, logistic_sgd.py, logistic_sgd.pyc, numofSen.py, outputs, para_reader.py, para_reader.pyc, plot.py, plotLines.py, rbm.py, rbm.pyc, saveCre.py

Authors Sukriti Verma, Vagisha Nidhi arXiv ID 1708.04439 Category cs.CL: Computation & Language Cross-listed cs.IR, cs.LG Citations 48 Venue Research on computing science Repository https://github.com/vagisha-nidhi/TextSummarizer ⭐ 19 Last Checked 1 month ago

Abstract

This paper proposes a text summarization approach for factual reports using a deep learning model. This approach consists of three phases: feature extraction, feature enhancement, and summary generation, which work together to assimilate core information and generate a coherent, understandable summary. We are exploring various features to improve the set of sentences selected for the summary, and are using a Restricted Boltzmann Machine to enhance and abstract those features to improve resultant accuracy without losing any important information. The sentences are scored based on those enhanced features and an extractive summary is constructed. Experimentation carried out on several articles demonstrates the effectiveness of the proposed approach. Source code available at: https://github.com/vagisha-nidhi/TextSummarizer