Extractive Document Summarization
12 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Extractive Document Summarization
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Libraries
Use these libraries to find Extractive Document Summarization models and implementations
CNN/Daily Mail
SubSumE
Most implemented papers
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).
Fine-tune BERT for Extractive Summarization
nlpyang/BertSum
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arXiv 2019
BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks.
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
kepingbi/ARedSumSentRank
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EACL 2021
Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018-
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LREC 2018
Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc.
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.
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization.
Heterogeneous Graph Neural Networks for Extractive Document Summarization
brxx122/HeterSUMGraph
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ACL 2020
An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships.
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
xssstory/STAS
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Findings of the Association for Computational Linguistics 2020
We also find in experiments that our model is less dependent on sentence positions.
GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state
Eulring/GoSum
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In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization.
