Extractive Document Summarization

9 papers with code • 1 benchmarks • 2 datasets

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Use these libraries to find Extractive Document Summarization models and implementations

Most implemented papers

Text Summarization with Pretrained Encoders

nlpyang/PreSumm 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 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 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- 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.

Neural Document Summarization by Jointly Learning to Score and Select Sentences

magic282/NeuSum ACL 2018

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

lliangchenc/DeepChannel 6 Nov 2018

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 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.