Abstractive Text Summarization
326 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
XL-Sum
Subtasks
Most implemented papers
TLDR: Extreme Summarization of Scientific Documents
We introduce TLDR generation, a new form of extreme summarization, for scientific papers.
Deep Reinforcement Learning For Sequence to Sequence Models
In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with sequence-to-sequence models that enable remembering long-term memories.
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
ChenRocks/fast_abs_rl
•
•
ACL 2018
Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i. e., compresses and paraphrases) to generate a concise overall summary.
Scoring Sentence Singletons and Pairs for Abstractive Summarization
ucfnlp/summarization-sing-pair-mix
•
•
ACL 2019
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
Unsupervised Opinion Summarization as Copycat-Review Generation
ixlan/CopyCat-abstractive-opinion-summarizer
•
•
ACL 2020
At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions.
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
microsoft/unilm
•
•
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
microsoft/HMNet
•
•
Findings of the Association for Computational Linguistics 2020
With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties.
Better Fine-Tuning by Reducing Representational Collapse
Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods.
DebateSum: A large-scale argument mining and summarization dataset
Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today.
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
makcedward/nlpaug
•
•
EMNLP 2021
We study generating abstractive summaries that are faithful and factually consistent with the given articles.
