The following are short summaries of some of the papers that I've read.

2018-09

• Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs

2018-07

• Learned in Translation: Contextualized Word Vectors
• Efficient and robust question answering from minimal context over documents

2017-08

• Get To The Point: Summarization with Pointer-Generator Networks

2017-07

• A Deep Reinforced Model for Abstractive Summarization
• Attention is All You Need
• Decoupling Encoder and Decoder Networks for Abstractive Document Summarization

2017-06

• Language as a Latent Variable: Discrete Generative Models for Sentence Compression

2017-03

• Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
• Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

2016-11

• N-gram-based Low-dimensional Representation for Document Classification

2016-08

• Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification

2016-05

• Document Classification by Combining Convolution and Recurrent Layers

2015-05

• Deep Residual Learning for Image Recognition

2015-02

• Convolutional Neural Network Architectures for Matching Natural Language Sentences

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