Extractive Summarization
100 papers with code • 0 benchmarks • 1 datasets
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SubSumE
Most implemented papers
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.
Leveraging BERT for Extractive Text Summarization on Lectures
This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection.
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
kedz/nnsum
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We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art.
Generating Wikipedia by Summarizing Long Sequences
tensorflow/tensor2tensor
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ICLR 2018
We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents.
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.
Diversity driven Attention Model for Query-based Abstractive Summarization
PrekshaNema25/DiverstiyBasedAttentionMechanism
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ACL 2017
Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion.
Extractive Summarization using Deep Learning
We are exploring various features to improve the set of sentences selected for the summary, and are using a Restricted Boltzmann Machine to enhance and abstract those features to improve resultant accuracy without losing any important information.
Self-Supervised Learning for Contextualized Extractive Summarization
hongwang600/Summarization
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ACL 2019
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.
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.
