Contextualized Rewriting for Text Summarization
Authors: Guangsheng Bao, Yue Zhang12544-12553
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our models are evaluated on the CNN/DM dataset (Hermann et al. 2015). Results show that the contextualized rewriter gives significantly improved ROUGE (Lin 2004) scores compared with a state-of-the-art extractive baseline, outperforming a traditional rewriter baseline by a large margin. |
| Researcher Affiliation | Academia | Guangsheng Bao1,2, Yue Zhang1,2 1 School of Engineering, Westlake University 2 Institute of Advanced Technology, Westlake Institute for Advanced Study {baoguangsheng, zhangyue}@westlake.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | Yes | We release our code at https://github.com/baoguangsheng/ctx-rewriter-for-summ.git. |
| Open Datasets | Yes | We evaluate our model on the CNN/Daily Mail dataset (Hermann et al. 2015), which comprises online news articles with several human written highlights (on average 3.75 per article). |
| Dataset Splits | Yes | We use the non-anonymized version and follow the standard splitting of Hermann et al. (2015), which includes 287,227 samples for training, 13,368 for dev testing, and 11,490 for testing. |
| Hardware Specification | Yes | The model is trained with 2 v100 GPUs for about 9 hours. (...) We train the model with 2 GPUs on a v100 machine for about 60 hours. |
| Software Dependencies | Yes | All scores are calculated using pyrouge. 1 (footnote 1: https://pypi.org/project/pyrouge/0.1.3/) |
| Experiment Setup | Yes | For inference, we select sentences according to the hyperparameters min sel = 3, max sel = 5 and threshold = 0.35, which are chosen by a grid search to find the best average score of ROUGE 1/2/L on the dev dataset. (...) The encoder and extractor are jointly trained for a total of 50,000 steps with a learning rate schedule (...) where warmup = 10, 000. |