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Abstractive Text Summarization

43 papers with code · Natural Language Processing
Subtask of Text Summarization

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Greatest papers with code

Pay Less Attention with Lightweight and Dynamic Convolutions

ICLR 2019 pytorch/fairseq

We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements.

ABSTRACTIVE TEXT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION

Classical Structured Prediction Losses for Sequence to Sequence Learning

NAACL 2018 pytorch/fairseq

There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam.

ABSTRACTIVE TEXT SUMMARIZATION MACHINE TRANSLATION STRUCTURED PREDICTION

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

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

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

Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

ACL 2018 ChenRocks/fast_abs_rl

Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i. e., compresses and paraphrases) to generate a concise overall summary.

ABSTRACTIVE TEXT SUMMARIZATION

Text Summarization with Pretrained Encoders

22 Aug 2019nlpyang/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

Global Encoding for Abstractive Summarization

ACL 2018 lancopku/Global-Encoding

To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context.

ABSTRACTIVE TEXT SUMMARIZATION

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

8 May 2019microsoft/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.

 SOTA for Text Summarization on GigaWord (using extra training data)

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

A Deep Reinforced Model for Abstractive Summarization

ICLR 2018 theamrzaki/text_summurization_abstractive_methods

We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL).

ABSTRACTIVE TEXT SUMMARIZATION