Document Summarization Using TextRank Based on Sentence Embedding

Jeong, Seokwon; Kim, Jintae; Kim, Harksoo

doi:10.5626/jok.2019.46.3.285

Cited by 2 publications

(1 citation statement)

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“…Our method uses a TextRank algorithm based on sentence embedding [19] to capture the meaning of a sentence. First, the method computes the cosine similarity between two sentence vectors created in Step 2.…”

Section: Sentence-to-sentence Cohesion Assessmentmentioning

confidence: 99%

Deep Learning-Based Bug Report Summarization Using Sentence Significance Factors

2022

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During the maintenance phase of software development, bug reports provide important information for software developers. Developers share information, discuss bugs, and fix associated bugs through bug reports; however, bug reports often include complex and long discussions, and developers have difficulty obtaining the desired information. To address this issue, researchers proposed methods for summarizing bug reports; however, to select relevant sentences, existing methods rely solely on word frequencies or other factors that are dependent on the characteristics of a bug report, failing to produce high-quality summaries or resulting in limited applicability. In this paper, we propose a deep-learning-based bug report summarization method using sentence significance factors. When conducting experiments over a public dataset using believability, sentence-to-sentence cohesion, and topic association as sentence significance factors, the results show that our method outperforms the state-of-the-art method BugSum with respect to precision, recall, and F-score and that the application scope of the proposed method is wider than that of BugSum.

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Section: Sentence-to-sentence Cohesion Assessmentmentioning

confidence: 99%