natural-language-processing-2

Sentence Summarization

3260 papers • 126 benchmarks • 313 datasets

Generating a summary of a given sentence.

(Image credit: Papersgraph)

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Subtasks

Unsupervised Sentence Summarization

Most implemented papers

Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

Çaglar Gülçehre, Ramesh Nallapati, Bowen Zhou, C. D. Santos, Bing Xiang•Thu Feb 18 2016

This work proposes several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time.

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A Neural Attention Model for Abstractive Sentence Summarization

S. Chopra, J. Weston, Alexander M. Rush•Tue Sep 01 2015

This work proposes a fully data-driven approach to abstractive sentence summarization by utilizing a local attention-based model that generates each word of the summary conditioned on the input sentence.

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A Regularized Framework for Sparse and Structured Neural Attention

Vlad Niculae, Mathieu Blondel•Sun Apr 30 2017

This paper proposes a new framework for sparse and structured attention, building upon a smoothed max operator, and shows that the gradient of this operator defines a mapping from real values to probabilities, suitable as an attention mechanism.

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Data-driven Summarization of Scientific Articles

Richard H. R. Hahnloser, Nikola I. Nikolov, Michael Pfeiffer•Mon Apr 23 2018

This work generates two novel multi-sentence summarization datasets from scientific articles and test the suitability of a wide range of existing extractive and abstractive neural network-based summarization approaches, demonstrating that scientific papers are suitable for data-driven text summarization.

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Selective Encoding for Abstractive Sentence Summarization

M. Zhou, Furu Wei, Nan Yang, Qingyu Zhou•Fri Mar 31 2017

The experimental results show that the proposed selective encoding model outperforms the state-of-the-art baseline models.

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Online and Linear-Time Attention by Enforcing Monotonic Alignments

Peter J. Liu, D. Eck, Ron J. Weiss, Colin Raffel, Minh-Thang Luong•Sun Apr 02 2017

This work proposes an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time and validates the approach on sentence summarization, machine translation, and online speech recognition problems.

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Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction

Raphael Schumann, K. Markert, Lili Mou, Olga Vechtomova, Yao Lu•Sun May 03 2020

This work proposes a new state-of-the art for unsupervised sentence summarization according to ROUGE scores, and demonstrates that the commonly reported RouGE F1 metric is sensitive to summary length.

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Sequential Copying Networks

M. Zhou, Furu Wei, Nan Yang, Qingyu Zhou•Wed Apr 25 2018

A novel copying framework, named Sequential Copying Networks (SeqCopyNet), which not only learns to copy single words, but also copies sequences from the input sentence and leverages the pointer networks to explicitly select a sub-span from the source side to target side.

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Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder

Raphael Schumann•Thu Sep 13 2018

An unsupervised approach to summarize sentences in abstractive way using Variational Autoencoder, showing that shorter sentences can not beat a simple baseline but yield higher ROUGE scores than trying to reconstruct the whole sentence.

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