mlsanityData to 7 Oct 2026

Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization

arXiv 2303.11660 ↗First posted 21 Mar 2023cs.CLcited by 2

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Abstract (arXiv)

Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews. However, generating aspect-specific and general summaries is challenging due to the lack of annotated data. In this work, we propose two simple yet effective unsupervised approaches to generate both aspect-specific and general opinion summaries by training on synthetic datasets constructed with aspect-related review contents. Our first approach, Seed Words Based Leave-One-Out (SW-LOO), identifies aspect-related portions of reviews simply by exact-matching aspect seed words and outperforms existing methods by 3.4 ROUGE-L points on SPACE and 0.5 ROUGE-1 point on OPOSUM+ for aspect-specific opinion summarization. Our second approach, Natural Language Inference Based Leave-One-Out (NLI-LOO) identifies aspect-related sentences utilizing an NLI model in a more general setting without using seed words and outperforms existing approaches by 1.2 ROUGE-L points on SPACE for aspect-specific opinion summarization and remains competitive on other metrics.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 57 references that are in the map. First posted on arXiv 21 Mar 2023.

Builds on

32 of 57 references.

References · 32

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20 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluationcited by 14
  2. 2024Product Description and QA Assisted Self-Supervised Opinion Summarizationcited by 6