Text Summarization

287 papers with code • 29 benchmarks • 75 datasets

Shortening a set of data computationally, to create a summary that represents the most important or relevant information within the original content (Source: Wikipedia).

Image source: LONG DOCUMENT SUMMARIZATION WITH TOP-DOWN AND BOTTOM-UP INFERENCE

Libraries

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.

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).

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.

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).

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.

Leveraging BERT for Extractive Text Summarization on Lectures

dmmiller612/lecture-summarizer 7 Jun 2019

This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection.