Multi-Document Summarization Based on Two-Level Sparse Representation Model
Authors: He Liu, Hongliang Yu, Zhi-Hong Deng
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on summarization benchmark data sets DUC2006 and DUC2007 show that our proposed model is effective and outperforms the state-of-the-art algorithms. |
| Researcher Affiliation | Academia | He Liu, Hongliang Yu, Zhi-Hong Deng Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China lhdgriver@gmail.com, yuhongliang324@gmail.com, zhdeng@cis.pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 MDS-Sparse Algorithm; Algorithm 2 Sparse Coding(S , S) |
| Open Source Code | No | The paper mentions the use of third-party tools with URLs (splitta and Porter Stemmer) but does not state that the code for their own described methodology is open-source or provide a link to it. |
| Open Datasets | Yes | In this study, we use the standard summarization benchmark DUC2006 and DUC2007 for evaluation. |
| Dataset Splits | No | The paper mentions using DUC2006 and DUC2007 datasets but does not specify details regarding train/validation/test splits or explicitly mention a validation set. |
| Hardware Specification | Yes | The experiments were performed on a 2.4GHz PC machine (Intel Core2 P8600) with 4GB of memory, running on an Ubuntu12.04 operating system. |
| Software Dependencies | No | The paper mentions 'splitta' and 'porter stemming algorithm' with URLs but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper mentions general parameters like 'sparse coefficient λ' and 'correlation coefficient β' (with β = 1000 through experiments) and some internal algorithm convergence criteria, but does not provide detailed hyperparameters, optimizer settings, or a comprehensive experimental setup section. |