# Semantic Extractor-Paraphraser based Abstractive Summarization

arXiv: https://arxiv.org/abs/2105.01296 · HTML: https://mlsanity.com/paper/2105.01296/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 15 of 15)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [BanditSum: Extractive Summarization as a Contextual Bandit](https://mlsanity.com/paper/1809.09672/) (2018)
- [Generative Adversarial Network for Abstractive Text Summarization](https://mlsanity.com/paper/1711.09357/) (2017)
- [Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation](https://mlsanity.com/paper/1907.12894/) (2019)
- [Multi-document abstractive summarization using ILP based multi-sentence compression](https://mlsanity.com/paper/1609.07034/) (2016)
- [Multi-Modal Summary Generation using Multi-Objective Optimization](https://mlsanity.com/paper/2005.09252/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Survey on Multi-modal Summarization](https://mlsanity.com/paper/2109.05199/) (2021)
- [WIDAR -- Weighted Input Document Augmented ROUGE](https://mlsanity.com/paper/2201.09282/) (2022)

## Abstract (arXiv)

The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploits semantic overlap as opposed to its predecessors that focus more on syntactic information overlap. Our model outperforms the state-of-the-art baselines in terms of ROUGE, METEOR and word mover similarity (WMS), establishing the superiority of the proposed system via extensive ablation experiments. We have also challenged the summarization capabilities of the state of the art Pointer Generator Network (PGN), and through thorough experimentation, shown that PGN is more of a paraphraser, contrary to the prevailing notion of a summarizer; illustrating it's incapability to accumulate information across multiple sentences.