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

Summary

The paper addresses how to condense many online product reviews into a short extractive summary of the most useful opinions without costly annotation. The authors propose a neural pipeline that relies only on cheap signals: product domain labels, star ratings supplied by reviewers, and a few seed words per aspect. Reviews are split into clause-like EDUs using an RST parser. Each segment is then processed by two components:

  • MATE, an aspect extractor that extends the ABAE autoencoder with seed words and a multi-task objective.
  • MILNET, a sentiment predictor trained with multiple instance learning on document ratings.

Segments that are aspect-specific and strongly polarized count as salient. A greedy selection step then builds the summary while avoiding redundancy. The authors also release OPOSUM, a corpus of Amazon reviews from six domains, with human-annotated development and test data. In automatic evaluation the system beat strong baselines on every stage. In a crowdsourced study, judges preferred it to the extractive and abstractive competitors.

Key points

  • OPOSUM corpus:
    • Amazon reviews covering six product domains.
    • Dev/test sets carry gold aspect labels, salience labels and multi-document extractive summaries.
    • Code and data are at github.com/stangelid/oposum.
  • Aspect extractor: MATE (Multi-Seed Aspect Extractor) builds on ABAE (He et al., 2017). Its aspect embeddings come from small keyword seed sets rather than k-means centroids alone, and training uses a multi-task objective.
  • Sentiment and selection: MILNET supplies segment polarity learned only from review ratings. The best configuration, MILNET+MATE+MT+RD, adds a redundancy filter.
  • Human evaluation:
    • Method: Best-Worst Scaling on Crowdflower, comparing the model against human summaries, Opinosis and the Lead baseline.
    • Criteria: informativeness, polarity, coherence and redundancy.
    • Results: the model beat both systems on all criteria (p < 0.05). Human summaries were significantly better only on coherence.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Sentiment & emotion · Summarization

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/d18-1403

@inproceedings{angelidis2018summarizing,
  title = {{Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised}},
  author = {Angelidis, Stefanos and Lapata, Mirella},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1403},
  url = {https://doi.org/10.18653/v1/d18-1403}
}

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