Comparative Document Summarisation via Classification
Authors: Umanga Bista, Alexander Mathews, Minjeong Shin, Aditya Krishna Menon, Lexing Xie20-28
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13 months. We observe that gradient-based optimisation outperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. |
| Researcher Affiliation | Academia | Umanga Bista, Alexander Mathews, Minjeong Shin, Aditya Krishna Menon, Lexing Xie Australian National University , Data to Decisions CRC {umanga.bista,alex.mathews,minjeong.shin,aditya.menon,lexing.xie}@anu.edu.au |
| Pseudocode | No | The paper describes algorithms but does not provide structured pseudocode or algorithm blocks labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code, datasets and a supplementary appendix are available at https://github.com/computationalmedia/compsumm |
| Open Datasets | Yes | Code, datasets and a supplementary appendix are available at https://github.com/computationalmedia/compsumm |
| Dataset Splits | Yes | For each news topic, we generate 10 random splits with 80% training articles and 20% test articles for automatic evaluation. |
| Hardware Specification | No | The paper mentions 'use of the Ne CTAR Research Cloud' but does not specify any particular hardware components like GPU or CPU models, or memory. |
| Software Dependencies | No | The paper mentions tools and algorithms like GloVe-300, L-BFGS, and k-means++ but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The hyper-parameter γ is chosen along with the trade-offfactor λ, and SVM soft margin C using grid search 3 fold cross-validation on the training set. |