5 citations · 9 across the 5 of their papers we have counts for
9 papers
Aspect-Controllable Opinion Summarization
Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata
Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them. In this paper, we propose an appro…
Convex Aggregation for Opinion Summarization
Hayate Iso, Xiaolan Wang, Yoshihiko Suhara +2
Recent advances in text autoencoders have significantly improved the quality of the latent space, which enables models to generate grammatical and consistent text from aggregated l…
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…
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…
Constructing Explainable Opinion Graphs from Review
Nofar Carmeli, Xiaolan Wang, Yoshihiko Suhara +4
The Web is a major resource of both factual and subjective information. While there are significant efforts to organize factual information into knowledge bases, there is much less…
OpinionDigest: A Simple Framework for Opinion Summarization
Yoshihiko Suhara, Xiaolan Wang, Stefanos Angelidis +1
We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment…
BookQA: Stories of Challenges and Opportunities
Stefanos Angelidis, Lea Frermann, Diego Marcheggiani +2
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to…
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
Stefanos Angelidis, Mirella Lapata
We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain…
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
Stefanos Angelidis, Mirella Lapata
We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and lear…