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..
SegBot: A Generic Neural Text Segmentation Model with Pointer Network
Authors: Jing Li, Aixin Sun, Shafiq Joty
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, SEGBOT outperforms state-of-the-art models on both topic and EDU segmentation tasks. We conduct two sets of experiments to evaluate the effectiveness of SEGBOT: segmenting a document into topically coherent segments, and segmenting a sentence into EDUs. |
| Researcher Affiliation | Academia | Jing Li, Aixin Sun and Shafiq Joty School of Computer Science and Engineering, Nanyang Technological University, Singapore EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model with equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'An online version of SEGBOT (EDU segmentation) is available at http://138.197.118.157:8000/segbot/.' This refers to a demo or online service, not the open-source code for the methodology itself. |
| Open Datasets | Yes | For evaluating topic segmentation models, we use the commonly used Choi dataset [Choi, 2000]. |
| Dataset Splits | Yes | We use the first 10% data of shuffled training set as development set for both Choi dataset and RST-DT dataset. |
| Hardware Specification | Yes | SEGBOT is implemented with Py Torch framework and evaluated on NVIDIA Tesla P100 GPU. |
| Software Dependencies | No | The paper states 'SEGBOT is implemented with Py Torch framework' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Table 1 gives the details of other hyper-parameter settings. We use Adam optimizer to update model parameters. In addition, we use gradient clipping by a max norm of 5 and l2 -regularization during training. |