Text Summarization

352 papers with code • 30 benchmarks • 84 datasets

Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.

There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.


Use these libraries to find Text Summarization models and implementations

Most implemented papers

Attention Is All You Need

tensorflow/tensor2tensor NeurIPS 2017

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

huggingface/transformers ACL 2020

We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.

Get To The Point: Summarization with Pointer-Generator Networks

abisee/pointer-generator ACL 2017

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).

Text Summarization with Pretrained Encoders

nlpyang/PreSumm IJCNLP 2019

For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not).

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

google-research/pegasus ICML 2020

Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization.

A Deep Reinforced Model for Abstractive Summarization

theamrzaki/text_summurization_abstractive_methods ICLR 2018

We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL).

Big Bird: Transformers for Longer Sequences

google-research/bigbird NeurIPS 2020

To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.

WikiHow: A Large Scale Text Summarization Dataset

mahnazkoupaee/WikiHow-Dataset 18 Oct 2018

Sequence-to-sequence models have recently gained the state of the art performance in summarization.

Unified Language Model Pre-training for Natural Language Understanding and Generation

microsoft/unilm NeurIPS 2019

This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.

Fastformer: Additive Attention Can Be All You Need

wuch15/Fastformer 20 Aug 2021

In this way, Fastformer can achieve effective context modeling with linear complexity.