Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection

• URL: http://arxiv.org/abs/2102.06856v1
• Date: Sat, 13 Feb 2021 03:50:31 GMT
• Title: Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection
• Authors: Wei Wang, Piji Li, Hai-Tao Zheng
• Abstract summary: We propose a reader-aware topic modeling and saliency information detection framework to enhance the quality of generated comments. For reader-aware topic modeling, we design a variational generative clustering algorithm for latent semantic learning and topic mining from reader comments. For saliency information detection, we introduce Bernoulli distribution estimating on news content to select saliency information.
• Score: 25.16392119801612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic comment generation is a special and challenging task to verify the model ability on news content comprehension and language generation. Comments not only convey salient and interesting information in news articles, but also imply various and different reader characteristics which we treat as the essential clues for diversity. However, most of the comment generation approaches only focus on saliency information extraction, while the reader-aware factors implied by comments are neglected. To address this issue, we propose a unified reader-aware topic modeling and saliency information detection framework to enhance the quality of generated comments. For reader-aware topic modeling, we design a variational generative clustering algorithm for latent semantic learning and topic mining from reader comments. For saliency information detection, we introduce Bernoulli distribution estimating on news content to select saliency information. The obtained topic representations as well as the selected saliency information are incorporated into the decoder to generate diversified and informative comments. Experimental results on three datasets show that our framework outperforms existing baseline methods in terms of both automatic metrics and human evaluation. The potential ethical issues are also discussed in detail.

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