Papers with Gigaword datasets

2 papers
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (P18-1)

Copied to clipboard

Challenge: Recent advances on abstractive summarization have allowed substantial improvements in the quality of the model, but there is still scope for improvement.
Approach: They propose novel multi-task architectures with high-level layer-specific sharing across multiple encoder and decoder layers of the three tasks and soft-sharing mechanisms.
Outcome: The proposed model improves on the CNN/DailyMail and Gigaword datasets and on the DUC-2002 transfer setup.
Concept Pointer Network for Abstractive Summarization (D19-1)

Copied to clipboard

Challenge: Abstractive summarization (ABS) has gained overwhelming success owing to a tremendous development of sequence-to-sequence models and its variants.
Approach: They propose a concept pointer network that leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts and then points to the most appropriate choice using both the concept set and original source text.
Outcome: The proposed model improves on the DUC-2004 and Gigaword datasets and human evaluation of its abstractive abilities supports the quality of the summaries produced.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations