Extractive Summarization

100 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

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.

Leveraging BERT for Extractive Text Summarization on Lectures

dmmiller612/lecture-summarizer 7 Jun 2019

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 14 Nov 2016

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 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 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 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

vagisha-nidhi/TextSummarizer 15 Aug 2017

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 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 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 ACL 2020

This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.