2018

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

Shi, Tian, Keneshloo, Yaser, Ramakrishnan, Naren et al.

Understand

In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity.

Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries.
Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding/generation.
There are also other concerns, such as efficiency and parallelism for training a model.

Built on

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