Unsupervised Opinion Summarization with Content Planning
Authors: Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata12489-12497
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three domains show that our approach outperforms competitive models in generating informative, coherent, and fluent summaries that capture opinion consensus. |
| Researcher Affiliation | Academia | Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh reinald.kim@ed.ac.uk, s.angelidis@ed.ac.uk, mlap@inf.ed.ac.uk |
| Pseudocode | No | The paper describes the model architecture and training process but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code can be downloaded from https://github.com/ rktamplayo/Plan Sum. |
| Open Datasets | Yes | We performed experiments on three opinion summarization benchmarks. These include the Rotten Tomatoes dataset1 (RT; Wang and Ling 2016) which contains a large set of reviews for various movies written by critics... Our second dataset is Yelp2 (Chu and Liu 2019)... Finally, the Amazon dataset3 (Braˇzinskas, Lapata, and Titov 2019)... |
| Dataset Splits | Yes | After applying these constraints we obtained 100k (Yelp), 25k (RT), and 90k (Amazon) review-summary pairs. Statistics of these datasets are reported in Table 1. |
| Hardware Specification | Yes | Our model was trained on a single Ge Force GTX 1080Ti GPU and is implemented using Py Torch. |
| Software Dependencies | No | Our model was trained on a single Ge Force GTX 1080Ti GPU and is implemented using Py Torch. We used the subword tokenizer of BERT (Devlin et al. 2019), which has a 30k token vocabulary trained using Word Piece (Wu et al. 2016). |
| Experiment Setup | Yes | Across models, we set all hidden dimensions to 256, the dropout rate to 0.1, and batch size to 16. We used the Adam optimizer (Kingma and Ba 2015) with a learning rate of 3e 4, l2 constraint of 3, and warmup of 8,000 steps. |