Generating a Structured Summary of Numerous Academic Papers: Dataset and Method
Authors: Shuaiqi LIU, Jiannong Cao, Ruosong Yang, Zhiyuan Wen
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our CAST method outperforms various advanced summarization methods. |
| Researcher Affiliation | Academia | Department of Computing, The Hong Kong Polytechnic University {cssqliu, csjcao, csryang, cszwen}@comp.polyu.edu.hk |
| Pseudocode | No | The paper does not include a figure, block, or section explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present structured steps formatted like code or an algorithm. |
| Open Source Code | No | The paper provides a link for the dataset ('Our dataset: https://github.com/StevenLau6/BigSurvey'), but it does not state that the source code for the proposed CAST methodology or the experimental setup is made publicly available or provide a link to such code. |
| Open Datasets | Yes | Our dataset: https://github.com/StevenLau6/BigSurvey |
| Dataset Splits | Yes | We split the training (80%), validation (10%), and test (10%) sets. |
| Hardware Specification | Yes | All the models are trained on one NVIDIA RTX8000. |
| Software Dependencies | No | The paper mentions using 'Hugging Face s Transformers [Wolf et al., 2020]' and 'fairseq [Ott and others, 2019]' for implementations, but it does not provide specific version numbers for these software libraries or other ancillary software components. |
| Experiment Setup | Yes | The vocabulary s maximum size is set as 50,265 for these abstractive summarization models, while the BERT-based classifiers use 30,522 as default. We use dropout with the probability 0.1. The optimizer is Adam with β1=0.9 and β2=0.999. Summarization models use learning rate of 5e 5, while the classifiers use 2e 5. We also adopt the learning rate warmup and decay. During decoding, we use beam search with a beam size of 5. Trigram blocking is used to reduce repetitions. |