187 citations · 1.6k across the 127 of their papers we have counts for
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Unsupervised Opinion Summarization with Content Planning
Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata
The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets. When summarizing reviews (e.g., for products…
Movie Summarization via Sparse Graph Construction
Pinelopi Papalampidi, Frank Keller, Mirella Lapata
We summarize full-length movies by creating shorter videos containing their most informative scenes. We explore the hypothesis that a summary can be created by assembling scenes wh…
Extractive Opinion Summarization in Quantized Transformer Spaces
Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara +2
We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization. QT is inspired by Vector-Quantized Variational Autoencoders, which we repurp…
Generating Query Focused Summaries from Query-Free Resources
Yumo Xu, Mirella Lapata
The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query…
Meta-Learning for Domain Generalization in Semantic Parsing
Bailin Wang, Mirella Lapata, Ivan Titov
The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-dom…
Compositional Generalization via Semantic Tagging
Hao Zheng, Mirella Lapata
Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i.e., they are unable to systematically g…