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Text Summarization

46 papers with code · Natural Language Processing

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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

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

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.

TEXT GENERATION TEXT SUMMARIZATION UNSUPERVISED MACHINE TRANSLATION

A Regularized Framework for Sparse and Structured Neural Attention

NeurIPS 2017 vene/sparse-structured-attention

Modern neural networks are often augmented with an attention mechanism, which tells the network where to focus within the input.

MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE TEXT SUMMARIZATION

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

5 Dec 2018tshi04/NATS

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

WikiHow: A Large Scale Text Summarization Dataset

18 Oct 2018mahnazkoupaee/WikiHow-Dataset

Sequence-to-sequence models have recently gained the state of the art performance in summarization.

TEXT SUMMARIZATION

Online and Linear-Time Attention by Enforcing Monotonic Alignments

ICML 2017 craffel/mad

Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems.

MACHINE TRANSLATION SPEECH RECOGNITION TEXT SUMMARIZATION

Pretraining-Based Natural Language Generation for Text Summarization

25 Feb 2019nayeon7lee/bert-summarization

For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence.

TEXT GENERATION TEXT SUMMARIZATION