Twitter Summarization Based on Social Network and Sparse Reconstruction
Authors: Ruifang He, Xingyi Duan
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on this datasets show the effectiveness of our framework for handling the large scale short and noisy messages in social media. |
| Researcher Affiliation | Academia | Ruifang He, Xingyi Duan School of Computer Science and Technology, Tianjin University, Tianjin 300350, China Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, China |
| Pseudocode | Yes | Algorithm 1 An Efficient Optimization Algorithm for SNSR |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | We use the public Twitter data collected by University of Illinois1 as the raw data. 1https://wiki.illinois.edu/wiki/display/forward/Dataset-UDI -Twitter Crawl-Aug2012 |
| Dataset Splits | No | The paper describes the creation of a "ground truth" corpus for evaluation, but does not specify explicit training, validation, and test splits for model development or hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software components with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | We tune four parameters greedily through setting step size, such as α in range [0, 1], setting step size as 0.01. Through preliminary experiments, we set α = 0.03, λ = 1, and γ = 1. And we set the similarity threshold θ = 0.1, which is consistent with the observation that the similarity between sentences is mostly distributed in the area of [0, 0.1]. |