Papers by Stefanos Angelidis
Book QA: Stories of Challenges and Opportunities (D19-58)
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| Challenge: | Existing approaches to answer questions based on the full text of books are limited by their unique characteristics. |
| Approach: | They propose a system for answering questions based on the full text of books . they use a memory network to reason and predict an answer, and a novel question generator to improve generalization. |
| Outcome: | The proposed system improves on the recently published NarrativeQA corpus on Who questions . it shows that the proposed system is highly challenging and needs more research . |
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised (D18-1)
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| Challenge: | Existing methods for opinion summarization are knowledge-lean and require light supervision. |
| Approach: | They propose a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision. |
| Outcome: | The proposed framework improves over baselines and shows that opinion summaries are preferred by human judges according to multiple criteria. |
OpinionDigest: A Simple Framework for Opinion Summarization (2020.acl-main)
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| Challenge: | Abstractive opinion summarization framework outperforms competitors' summarizing frameworks . extractive approaches produce well-formed text, but selecting the most popular opinions is challenging . |
| Approach: | They propose an abstractive opinion summarization framework that trains a Transformer model to reconstruct reviews from extracted opinions. |
| Outcome: | The proposed framework outperforms baselines on Yelp and shows promising customization capabilities. |
Convex Aggregation for Opinion Summarization (2021.findings-emnlp)
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| Challenge: | Recent advances in text autoencoders have significantly improved the quality of the latent space, allowing models to generate consistent text from aggregated latent vectors. |
| Approach: | They develop a framework which searches input-output word overlap for latent vector aggregation. |
| Outcome: | The proposed framework improves the quality of the latent space and establishes state-of-the-art performance on two opinion summarization benchmarks. |
Aspect-Controllable Opinion Summarization (2021.emnlp-main)
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| Challenge: | Recent work on opinion summarization produces general summaries based on reviews and popularity of opinions expressed in them. |
| Approach: | They propose an approach that generates customized opinion summaries based on aspect queries. |
| Outcome: | The proposed model outperforms the current state of the art and generates personalized summaries by controlling the number of aspects discussed in them. |
Extractive Opinion Summarization in Quantized Transformer Spaces (2021.tacl-1)
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| Challenge: | Existing work on opinion summarization focuses on aggregating opinions among reviews . et al., 2018; see etal., 2019; liu eto, 2019) demonstrate the potential of opinion summaries. |
| Approach: | They propose an unsupervised system for extractive opinion summarization based on vector-quantized variables and an extraction algorithm. |
| Outcome: | The proposed method is validated by human studies showing that judges prefer it over baselines. |
Comparative Opinion Summarization via Collaborative Decoding (2022.findings-acl)
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| Challenge: | Existing opinion summarization methods are insufficient to help users compare multiple choices. |
| Approach: | They propose a comparative opinion summarization task that generates two contrastive summaries and one common summary from two different candidate sets of reviews. |
| Outcome: | The proposed framework produces higher-quality contrastive and common summaries than state-of-the-art models. |