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Extractive Document Summarization

7 papers with code · Natural Language Processing
Subtask of Text Summarization

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Fine-tune BERT for Extractive Summarization

arXiv 2019 nlpyang/BertSum

BERT (Devlin et al., 2018), a pre-trained Transformer (Vaswani et al., 2017) model, has achieved ground-breaking performance on multiple NLP tasks.

#3 best model for Document Summarization on CNN / Daily Mail (using extra training data)

DOCUMENT SUMMARIZATION EXTRACTIVE DOCUMENT SUMMARIZATION

Fine-tune BERT for Extractive Summarization

arXiv 2019 nlpyang/BertSum

BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks.

EXTRACTIVE DOCUMENT SUMMARIZATION

Text Summarization with Pretrained Encoders

IJCNLP 2019 nlpyang/PreSumm

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).

 SOTA for Extractive Document Summarization on CNN / Daily Mail (using extra training data)

ABSTRACTIVE TEXT SUMMARIZATION DOCUMENT SUMMARIZATION EXTRACTIVE DOCUMENT SUMMARIZATION

Neural Document Summarization by Jointly Learning to Score and Select Sentences

ACL 2018 magic282/NeuSum

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.

DOCUMENT SUMMARIZATION EXTRACTIVE DOCUMENT SUMMARIZATION

Iterative Document Representation Learning Towards Summarization with Polishing

EMNLP 2018 yingtaomj/Iterative-Document-Representation-Learning-Towards-Summarization-with-Polishing

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.

EXTRACTIVE DOCUMENT SUMMARIZATION REPRESENTATION LEARNING

DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization

6 Nov 2018lliangchenc/DeepChannel

We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization.

DOCUMENT SUMMARIZATION EXTRACTIVE DOCUMENT SUMMARIZATION

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

LREC 2018 edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-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 SUMMARIZATION EXTRACTIVE DOCUMENT SUMMARIZATION