Keywords-Guided Abstractive Sentence Summarization
Authors: Haoran Li, Junnan Zhu, Jiajun Zhang, Chengqing Zong, Xiaodong He8196-8203
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that multi-task learning and keywords-oriented guidance facilitate sentence summarization task, achieving better performance than the competitive models on the English Gigaword sentence summarization dataset. |
| Researcher Affiliation | Collaboration | 1JD AI Research 2National Laboratory of Pattern Recognition, Institute of Automation, CAS 3University of Chinese Academy of Sciences 4CAS Center for Excellence in Brain Science and Intelligence Technology |
| Pseudocode | No | The paper describes the model architecture and steps in narrative text and formulas, but does not provide an explicitly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We conduct experiments on the English Gigaword dataset, which has about 3.8 million training sentence-summary pairs. We use 8, 000 pairs as the validation set and 2, 000 pairs as the test set, provided by Zhou et al. (2017). |
| Dataset Splits | Yes | We use 8, 000 pairs as the validation set and 2, 000 pairs as the test set, provided by Zhou et al. (2017). |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions using an 'Adam optimizer' and 'dropout' but does not specify software dependencies with version numbers (e.g., Python version, specific library versions like PyTorch or TensorFlow). |
| Experiment Setup | Yes | We set the size of word embedding and LSTM hidden state to 300 and 512, respectively. Adam optimizer is applied with the learning rate of 0.0005, momentum parameters β1 = 0.9 and β1 = 0.999, and ϵ = 10 8. We use dropout (Srivastava et al. 2014) with probability of 0.2 and gradient clipping (Pascanu, Mikolov, and Bengio 2013) with range [ 1, 1]. The mini-batch size is set to 64. |