Research on Multi-document Summarization Based on LDA Topic Model

Bian, Jinqiang; Jiang, Zengru; Chen, Qian

doi:10.1109/ihmsc.2014.130

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…ES-LDA [9] only uses probability distributions when ranks RDF triples. However, Bian et al [1] combined topic-importance and topic-distribution for sentence-ranking problem and got a better performance. Since each element of an RDF triple has different role, the influence of various elements in RDF triples cannot be regarded as the same.…”

Section: Proposed Modelmentioning

confidence: 99%

MPSUM: Entity Summarization with Predicate-based Matching

Wei¹,

Gao²,

Liu³

et al. 2020

Preprint

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With the development of Semantic Web, entity summarization has become an emerging task to generate concrete summaries for real world entities. To solve this problem, we propose an approach named MPSUM that extends a probabilistic topic model by integrating the idea of predicate-uniqueness and object-importance for ranking triples. The approach aims atwebviewpanel:webview-panel/webview-0fb291c9-310d-42e2-a420-ed1a22b0a5ac generating brief but representative summaries for entities. We compare our approach with the state-of-the-art methods using DBpedia and LinkedMDB datasets. The experimental results show that our work improves the quality of entity summarization. The source code and outputs are available at https: //github.com/WeiDongjunGabriel/MPSUM 1 .

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Section: Proposed Modelmentioning

confidence: 99%

MPSUM: Entity Summarization with Predicate-based Matching

Wei¹,

Gao²,

Liu³

et al. 2020

Preprint

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“…Generally, the higher the probability is, the stronger the relationship between topic and reviewer is. We calculate multiple topic probabilities (Bian et al 2014) with PRs that reviewed by the same collaborator, since each collaborator has reviewed a lot of PRs. Usually, the importance of the topic is different in different PRs, and the importance of the topic is related to the length of the text of PR.…”

Section: Pr Topic-collaborator Relation Matrix Constructionmentioning

confidence: 99%

Core-reviewer recommendation based on Pull Request topic model and collaborator social network

Liao

Zexuan

et al. 2019

Soft Comput

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Pull Request (PR) is a major contributor to external developers of open-source projects in GitHub. PR reviewing is an important part of open-source software developments to ensure the quality of project. Recommending suitable candidates of reviewer to the new PRs will make the PR reviewing more efficient. However, there is not a mechanism of automatic reviewer recommendation for PR in GitHub. In this paper, we propose an automatic core-reviewer recommendation approach, which combines PR topic model with collaborators in the social network. First PR topics will be extracted from PRs by the latent Dirichlet allocation, and then the collaborator-PR network will be constructed with the connection between collaborators and PRs, and the influence of each collaborator will be calculated via the improved PageRank algorithm which combines with HITS. Finally, the relationship between topics and collaborators will also be built by the history of PR reviewing. When a new PR presents, a collaborator will be chosen as a core reviewer according to the influence of collaborators and the relationship between the new PR and collaborators. The experiment results show in the matching score calculation processing, the influence of collaborators shows higher than that with the expert, and the recommendation precision is better than 70%.

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“…Therefore, the probability of topics can reflect the relevance between topics and collaborators in each PR. However there are many PRs, so we need to calculate the topic-importance [17] of multi-document.…”

Section: B Relation Matrix Constructionmentioning

confidence: 99%

Topic-Based Integrator Matching for Pull Request

Liao

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator.

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Research on Multi-document Summarization Based on LDA Topic Model

Cited by 12 publications

References 6 publications

MPSUM: Entity Summarization with Predicate-based Matching

MPSUM: Entity Summarization with Predicate-based Matching

Core-reviewer recommendation based on Pull Request topic model and collaborator social network

Topic-Based Integrator Matching for Pull Request

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