Reader-Aware Multi-Document Summarization via Sparse Coding
Authors: Piji Li, Lidong Bing, Wai Lam, Hang Li, Yi Liao
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on this data set and some classical data sets demonstrate the effectiveness of our proposed approach. (Abstract) 3 Experiments |
| Researcher Affiliation | Collaboration | Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA USA Noah s Ark Lab, Huawei Technologies, Hong Kong {pjli, wlam, yliao}@se.cuhk.edu.hk, lbing@cs.cmu.edu, hangli.hl@huawei.com |
| Pseudocode | Yes | Algorithm 1 Coordinate descent algorithm for sentence expressiveness detection |
| Open Source Code | No | The paper does not provide any link or statement indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on our data set and some benchmark data sets have been conducted to examine the efficacy of our framework. (Section 1) DUC. In order to show that our sparse coding based framework can also work well on traditional MDS task, we employ the benchmark data sets DUC 2006 and DUC 2007 for evaluation. (Section 3.1) |
| Dataset Splits | No | Our data set... We also have a separate development (tuning) set containing 24 topics and each topic has one model summary. (Section 3.1) While a development set is mentioned, specific train/validation/test percentages or sample counts for the new dataset or DUC datasets are not provided. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or cloud resources) used for running the experiments. |
| Software Dependencies | Yes | In the implementation, we use a package called lp solve2. (Footnote 2: http://lpsolve.sourceforge.net/5.5/) |
| Experiment Setup | Yes | Parameter settings. We set C = 0.8 and p = 4 in the position weight function. For the sparse coding model, we set the stopping criteria T = 300, ε = 10 4, and the learning rate η = 1. For the sparsity item penalty, we set λ = 0.005. |