Note this forthcoming D4 seminar on Tuesday, 17th April 2018 at 11am, room A008.
It will be given by Angela Fan, Facebook AI Research.
Sequence to Sequence Learning for User-Controllable Abstractive Summarization
The design of neural architectures for sequence to sequence tasks such as summarization is an active research field. I will first briefly discuss the architectural changes that enable our convolutional sequence to sequence model, such as replacing non-linearities with novel gated linear units and multi-hop attention. The second part of the talk will discuss ways to train models for large-scale summarization tasks and respect user preferences. Our model enables users to specify high-level attributes to control the shape of final summaries to suit their needs. For example, users may want to specify the length or portion of the document to summarize. With this input, the system can produce summaries that respect user preference. Without user input, the control variables can be automatically set to outperform comparable state of the art summarization models.