Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning towards Abstractive Timeline Summarization
Authors: Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, Rui Yan
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations. |
| Researcher Affiliation | Academia | 1Center for Data Science, Peking University, Beijing, China 2Institute of Computer Science and Technology, Peking University, Beijing, China |
| Pseudocode | No | The paper describes the model architecture and components in text and diagrams, but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | We also release the first real-world large-scale timeline summarization dataset1. 1http://tiny.cc/lfh56y |
| Open Datasets | Yes | We also release the first real-world large-scale timeline summarization dataset1. 1http://tiny.cc/lfh56y |
| Dataset Splits | Yes | In total, our training dataset amounts to 169,423 samples with 5,000 evaluation and 5,000 test samples. |
| Hardware Specification | Yes | We implement our experiments in Tensor Flow [Abadi et al., 2016] on NVIDIA GTX 1080 Ti GPU. |
| Software Dependencies | No | We implement our experiments in Tensor Flow [Abadi et al., 2016] on NVIDIA GTX 1080 Ti GPU. |
| Experiment Setup | Yes | The word embedding dimension is set to 128 and the number of hidden units is 256. For time-event memory, the dimension of key, global value, and local value is 128, 512, and 256 respectively. We initialize all of the parameters randomly using an uniform distribution in [-0.02, 0.02]. The batch size is set to 16, and the event number is set to 8. We use Adagrad optimizer [Duchi et al., 2010] as our optimizing algorithm and the learning rate is 0.15. In decoding, we employ beam search with beam size 4 to generate more fluency summary sentence. |