Abstractive Text Summarization
325 papers with code • 19 benchmarks • 48 datasets
Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.
Source: Generative Adversarial Network for Abstractive Text Summarization
Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Benchmarks
These leaderboards are used to track progress in Abstractive Text Summarization
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Libraries
Use these libraries to find Abstractive Text Summarization models and implementationsDatasets
Reddit
CNN/Daily Mail
New York Times Annotated Corpus
WikiHow
SAMSum
NEWSROOM
LCSTS
WikiSum
WikiLingua
Reddit TIFU
Subtasks
Most implemented papers
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
tshi04/NATS
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As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization.
Encode, Tag, Realize: High-Precision Text Editing
google-research/lasertagger
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IJCNLP 2019
We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task.
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
theamrzaki/text_summurization_abstractive_methods
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CONLL 2016
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.
Global Encoding for Abstractive Summarization
lancopku/Global-Encoding
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ACL 2018
To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context.
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
Pay Less Attention with Lightweight and Dynamic Convolutions
We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements.
Pretraining-Based Natural Language Generation for Text Summarization
nayeon7lee/bert-summarization
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CONLL 2019
For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence.
Evaluating the Factual Consistency of Abstractive Text Summarization
yuhui-zh15/FactCCX
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EMNLP 2020
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
