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    <abstract>Recently research in opinions summarization focuses on rating expressions by aspects and/or sentiments they carry. To extract aspects of an expression, most studies require a predefined list of aspects or at least the number of aspects. Instead of extracting aspects, we rate expressions by aspect similarity recognition (ASR), which evaluates whether two expressions share at least one aspect. This subtask relaxes the limitation of predefining aspects and makes our opinions summarization applicable in domain adaptation. For the ASR subtask, we propose an attention-cell LSTM model, which integrates attention signals into the LSTM gates. According to the experimental results, the attention-cell LSTM works efficiently for learning latent aspects between two sentences in both settings of in-domain and cross-domain. In addition, the proposed extractive summarization method using ASR shows significant improvements over baselines on the Opinosis corpus.</abstract>
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%0 Conference Proceedings
%T Opinions Summarization: Aspect Similarity Recognition Relaxes The Constraint of Predefined Aspects
%A Huy Tien, Nguyen
%A Tung Thanh, Le
%A Minh Le, Nguyen
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F huy-tien-etal-2019-opinions
%X Recently research in opinions summarization focuses on rating expressions by aspects and/or sentiments they carry. To extract aspects of an expression, most studies require a predefined list of aspects or at least the number of aspects. Instead of extracting aspects, we rate expressions by aspect similarity recognition (ASR), which evaluates whether two expressions share at least one aspect. This subtask relaxes the limitation of predefining aspects and makes our opinions summarization applicable in domain adaptation. For the ASR subtask, we propose an attention-cell LSTM model, which integrates attention signals into the LSTM gates. According to the experimental results, the attention-cell LSTM works efficiently for learning latent aspects between two sentences in both settings of in-domain and cross-domain. In addition, the proposed extractive summarization method using ASR shows significant improvements over baselines on the Opinosis corpus.
%R 10.26615/978-954-452-056-4_058
%U https://aclanthology.org/R19-1058
%U https://doi.org/10.26615/978-954-452-056-4_058
%P 487-496

Markdown (Informal)

[Opinions Summarization: Aspect Similarity Recognition Relaxes The Constraint of Predefined Aspects](https://aclanthology.org/R19-1058) (Huy Tien et al., RANLP 2019)

• Opinions Summarization: Aspect Similarity Recognition Relaxes The Constraint of Predefined Aspects (Huy Tien et al., RANLP 2019)

ACL

• Nguyen Huy Tien, Le Tung Thanh, and Nguyen Minh Le. 2019. Opinions Summarization: Aspect Similarity Recognition Relaxes The Constraint of Predefined Aspects. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 487–496, Varna, Bulgaria. INCOMA Ltd..