Search Results for author: Stefanos Angelidis

Found 11 papers, 8 papers with code

Comparative Opinion Summarization via Collaborative Decoding

1 code implementation Findings (ACL) 2022 Hayate Iso, Xiaolan Wang, Stefanos Angelidis, Yoshihiko Suhara

Opinion summarization focuses on generating summaries that reflect popular subjective information expressed in multiple online reviews.

Opinion Summarization

Aspect-Controllable Opinion Summarization

1 code implementation EMNLP 2021 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.

Opinion Summarization

Convex Aggregation for Opinion Summarization

1 code implementation Findings (EMNLP) 2021 Hayate Iso, Xiaolan Wang, Yoshihiko Suhara, Stefanos Angelidis, Wang-Chiew Tan

We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected $L_2$-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration.

Opinion Summarization Unsupervised Opinion Summarization

Unsupervised Opinion Summarization with Content Planning

1 code implementation14 Dec 2020 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.

Abstractive Text Summarization Opinion Summarization +1

OpinionDigest: A Simple Framework for Opinion Summarization

1 code implementation ACL 2020 Yoshihiko Suhara, Xiaolan Wang, Stefanos Angelidis, Wang-Chiew Tan

The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

Book QA: Stories of Challenges and Opportunities

no code implementations WS 2019 Stefanos Angelidis, Lea Frermann, Diego Marcheggiani, Roi Blanco, Llu{\'\i}s M{\`a}rquez

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 reason and predict an answer.

Retrieval

BookQA: Stories of Challenges and Opportunities

no code implementations2 Oct 2019 Stefanos Angelidis, Lea Frermann, Diego Marcheggiani, Roi Blanco, Lluís Màrquez

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 reason and predict an answer.

Retrieval

Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised

2 code implementations EMNLP 2018 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 labels and user-provided ratings).

Aspect Extraction Multiple Instance Learning +1

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