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Text Summarization

71 papers with code · Natural Language Processing

Text summarization is the task of distilling noteworthy information in a document to produce an abridged version of it

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Levenshtein Transformer

NeurIPS 2019 pytorch/fairseq

We further confirm the flexibility of our model by showing a Levenshtein Transformer trained by machine translation can straightforwardly be used for automatic post-editing.

AUTOMATIC POST-EDITING MACHINE TRANSLATION TEXT SUMMARIZATION

Get To The Point: Summarization with Pointer-Generator Networks

ACL 2017 abisee/pointer-generator

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).

ABSTRACTIVE TEXT SUMMARIZATION

Neural Abstractive Text Summarization with Sequence-to-Sequence Models: A Survey

5 Dec 2018shibing624/pycorrector

As part of this survey, we also develop an open source library, namely Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization.

ABSTRACTIVE TEXT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION

Unified Language Model Pre-training for Natural Language Understanding and Generation

NeurIPS 2019 microsoft/unilm

This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.

#2 best model for Text Summarization on GigaWord (using extra training data)

ABSTRACTIVE TEXT SUMMARIZATION DOCUMENT SUMMARIZATION LANGUAGE MODELLING QUESTION ANSWERING QUESTION GENERATION TEXT GENERATION

MASS: Masked Sequence to Sequence Pre-training for Language Generation

7 May 2019microsoft/MASS

Pre-training and fine-tuning, e. g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks.

CONVERSATIONAL RESPONSE GENERATION TEXT GENERATION TEXT SUMMARIZATION UNSUPERVISED MACHINE TRANSLATION

Deep Reinforcement Learning For Sequence to Sequence Models

24 May 2018yaserkl/RLSeq2Seq

In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with sequence-to-sequence models that enable remembering long-term memories.

ABSTRACTIVE TEXT SUMMARIZATION DECISION MAKING MACHINE TRANSLATION

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

Leveraging BERT for Extractive Text Summarization on Lectures

7 Jun 2019dmmiller612/bert-extractive-summarizer

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.

TEXT SUMMARIZATION

VizSeq: A Visual Analysis Toolkit for Text Generation Tasks

IJCNLP 2019 facebookresearch/vizseq

Automatic evaluation of text generation tasks (e. g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE.

IMAGE CAPTIONING MACHINE TRANSLATION TEXT GENERATION TEXT SUMMARIZATION VIDEO DESCRIPTION

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

CONLL 2016 theamrzaki/text_summurization_abstractive_methods

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.

ABSTRACTIVE TEXT SUMMARIZATION