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Found 15 papers, 3 papers with code
Generating Abstractive Summaries with Finetuned Language Models
Neural abstractive document summarization is commonly approached by models that exhibit a mostly extractive behavior.
Debugging Sequence-to-Sequence Models with Seq2Seq-Vis
Neural attention-based sequence-to-sequence models (seq2seq) (Sutskever et al., 2014; Bahdanau et al., 2014) have proven to be accurate and robust for many sequence prediction tasks.
Training for Diversity in Image Paragraph Captioning
Image paragraph captioning models aim to produce detailed descriptions of a source image.
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU
We present a system description of the OpenNMT Neural Machine Translation entry for the WNMT 2018 evaluation.
The Annotated Transformer
A major goal of open-source NLP is to quickly and accurately reproduce the results of new work, in a manner that the community can easily use and modify.
Coarse-to-Fine Attention Models for Document Summarization
Sequence-to-sequence models with attention have been successful for a variety of NLP problems, but their speed does not scale well for tasks with long source sequences such as document summarization.
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Document Summarization
on CNN / Daily Mail
