Extractive Text Summarization

27 papers with code • 3 benchmarks • 4 datasets

Given a document, selecting a subset of the words or sentences which best represents a summary of the document.

Libraries

Use these libraries to find Extractive Text Summarization models and implementations

Most implemented papers

Get To The Point: Summarization with Pointer-Generator Networks

abisee/pointer-generator ACL 2017

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).

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.

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.

A Neural Attention Model for Abstractive Sentence Summarization

tensorflow/models EMNLP 2015

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build.

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.

DebateSum: A large-scale argument mining and summarization dataset

arvind-balaji/debate-cards COLING (ArgMining) 2020

Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today.

Centroid-based Text Summarization through Compositionality of Word Embeddings

gaetangate/text-summarizer WS 2017

The textual similarity is a crucial aspect for many extractive text summarization methods.

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.

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.