Papers by Arthur Bražinskas
Embedding Words as Distributions with a Bayesian Skip-gram Model (C18-1)
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| Challenge: | Rather than assuming that word embeddings are fixed across the entire text collection, we generate them from word-specific prior densities for each word. |
| Approach: | They propose a method for embedding words as probability densities in a low-dimensional space from a word-specific prior density for each occurrence of a given word. |
| Outcome: | The proposed method can encode word as a distribution on a range of benchmarks and is comparable to Gaussian embeddings. |
Few-Shot Learning for Opinion Summarization (2020.emnlp-main)
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| Challenge: | a recent study shows that abstractive summarization models fail to capture their essential properties due to the high cost of summary production. |
| Approach: | They propose a few-shot framework for abstractive opinion summarization that bootstraps the output of an unsupervised model. |
| Outcome: | The proposed framework outperforms extractive and abstractive methods on Amazon and Yelp datasets. |
Unsupervised Opinion Summarization as Copycat-Review Generation (2020.acl-main)
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| Challenge: | Recent work on opinion summarization has focused on extracting fragments from reviews, but we use novel sentences to generate abstractive summaries. |
| Approach: | They propose an abstractive summarizer which does not use summaries in training and is trained end-to-end on a large collection of reviews. |
| Outcome: | The proposed model produces fluent and coherent summaries reflecting consensus opinions on Amazon and Yelp reviews. |
Learning Opinion Summarizers by Selecting Informative Reviews (2021.emnlp-main)
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| Challenge: | supervised summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. |
| Approach: | They propose to combine a large dataset of opinion summaries with user reviews to form a supervised summarizer. |
| Outcome: | The proposed method improves the quality of summarization and reduces hallucinations in the summarizer. |