Extractive Text Summarization
32 papers with code • 5 benchmarks • 5 datasets
Given a document, selecting a subset of the words or sentences which best represents a summary of the document.
Benchmarks
These leaderboards are used to track progress in Extractive Text Summarization
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Libraries
Use these libraries to find Extractive Text Summarization models and implementations
CNN/Daily Mail
GovReport
DUC 2004
DebateSum
Most implemented papers
Searching for Effective Neural Extractive Summarization: What Works and What's Next
maszhongming/Effective_Extractive_Summarization
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ACL 2019
The recent years have seen remarkable success in the use of deep neural networks on text summarization.
Extractive Summarization as Text Matching
maszhongming/MatchSum
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ACL 2020
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.
Screenplay Summarization Using Latent Narrative Structure
Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront.
CX DB8: A queryable extractive summarizer and semantic search engine
Competitive Debate's increasingly technical nature has left competitors looking for tools to accelerate evidence production.
Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
Ranking Sentences for Extractive Summarization with Reinforcement Learning
shashiongithub/Refresh
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NAACL 2018
In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.
Document Modeling with External Attention for Sentence Extraction
shashiongithub/Document-Models-with-Ext-Information
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ACL 2018
Document modeling is essential to a variety of natural language understanding tasks.
Neural Document Summarization by Jointly Learning to Score and Select Sentences
In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences.
BanditSum: Extractive Summarization as a Contextual Bandit
yuedongP/BanditSum
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EMNLP 2018
In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels.
Iterative Document Representation Learning Towards Summarization with Polishing
yingtaomj/Iterative-Document-Representation-Learning-Towards-Summarization-with-Polishing
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EMNLP 2018
In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.
