Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond | TDLS

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=i29ckCz1XTw


Duration: 1:00:38
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97

Toronto Deep Learning Series, 7 August 2018

For slides and more information, visit https://tdls.a-i.science/events/2018-08-07/

Paper Review: https://arxiv.org/abs/1602.06023

Speaker: https://www.linkedin.com/in/ehsanamjadian/
Organizer: https://www.linkedin.com/in/amirfz/

Host: PWC

Paper abstract:
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.



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Tags:
deep learning
nlp
text summarization
rnn
sequence models
sequence to sequence