Relatedness-based Multi-Entity Summarization
Authors: Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches. |
| Researcher Affiliation | Academia | Kalpa Gunaratna1, Amir Hossein Yazdavar1, Krishnaprasad Thirunarayan1, Amit Sheth1, and Gong Cheng2 1Kno.e.sis, Wright State University, Dayton OH, USA 2National Key Laboratory for Novel Software Technology, Nanjing University, China {kalpa,amir,tkprasad,amit}@knoesis.org, gcheng@nju.edu.cn |
| Pseudocode | No | The paper describes the GRASP algorithm and its modifications using equations and text but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | We used DBpedia (version 2016-04) encyclopedic dataset as our knowledge graph to retrieve entity descriptions and ran the RDF2Vec model on it. For the semantic relatedness measure, we used the Word Net lexical database. We used two document samples taken from two popular entity linking benchmark datasets: (i) Wikinews 3 (20 documents) and (ii) AQUAINT 4 (10 documents). ... 3http://www.newsreader-project.eu/results/data/wikinews 4http://www.nzdl.org/wikification/docs.html |
| Dataset Splits | No | The paper mentions using Wikinews and AQUAINT datasets for evaluation but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions using the RDF2Vec model and Word Net lexical database but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In our implementation of memory-based GRASP algorithm, we set γ, β, λ, σ to 1, 3, 5, and 5, respectively (as suggested by authors of GRASP). ... In the greedy ranking function shown in Equation 5, we set τ = 1 and φ = 0.5. In the profit matrix, we used α = 2, β = 1, and γ = 1.5. We set the threshold η = 0.45. |