Text Summarization
395 papers with code • 33 benchmarks • 91 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.
Benchmarks
These leaderboards are used to track progress in Text Summarization
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Libraries
Use these libraries to find Text Summarization models and implementationsDatasets
Pubmed
Reddit
CNN/Daily Mail
New York Times Annotated Corpus
CORD-19
S2ORC
WikiHow
Multi-News
NEWSROOM
Subtasks
Most implemented papers
Attention Is All You Need
tensorflow/tensor2tensor
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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
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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
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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
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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
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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.
Big Bird: Transformers for Longer Sequences
google-research/bigbird
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NeurIPS 2020
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
A Deep Reinforced Model for Abstractive Summarization
theamrzaki/text_summurization_abstractive_methods
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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
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
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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
In this way, Fastformer can achieve effective context modeling with linear complexity.
