# Extractive Summarization using Deep Learning

arXiv: https://arxiv.org/abs/1708.04439 · HTML: https://mlsanity.com/paper/1708.04439/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2017.

## Built on by (most cited first, 8 of 8)

- [Bringing order into the realm of Transformer-based language models for artificial intelligence and law](https://mlsanity.com/paper/2308.05502/) (2023)
- [Robust Deep Reinforcement Learning for Extractive Legal Summarization](https://mlsanity.com/paper/2111.07158/) (2021)
- [An Evaluation Framework for Legal Document Summarization](https://mlsanity.com/paper/2205.08478/) (2022)
- [Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks](https://mlsanity.com/paper/2305.15044/) (2023)
- [Word-level Text Highlighting of Medical Texts for Telehealth Services](https://mlsanity.com/paper/2105.10400/) (2021)
- [Extractive Summarization of Call Transcripts](https://mlsanity.com/paper/2103.10599/) (2021)
- [BERT-VBD: Vietnamese Multi-Document Summarization Framework](https://mlsanity.com/paper/2409.12134/) (2024)
- [Comparing Methods for Extractive Summarization of Call Centre Dialogue](https://mlsanity.com/paper/2209.02472/) (2022)

## Abstract (arXiv)

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