## 1 Introduction  
Bug reports are an essential resource for long-term maintenance of software systems. Developers share information, discuss bugs, and fix associated bugs through bug reports [1]. Bug reports are managed by using bug tracking systems such as Trac and Bugzilla. Figure 1 shows an example of a bug report posted on the Bugzilla platform. As shown in Figure 1, the bug report contains a detailed description of the bug and a list of comments that contain the developers' discussion on how to reproduce or how to fix the bug. Bug reports are structured in the form of public posts with discussion sections similar to posts of social websites [2]. Because bug reports are written in the form of discussions among many people, developers often spend much time perusing lengthy bug reports and encounter difficulty obtaining the desired information.  
To address these problems, various studies [3, 4, 5, 6, 7, 8, 9, 10] have proposed methods for summarizing bug reports; however, most existing methods relied on either training data [3] or word frequencies [4], failing to produce high-quality summaries. As a method that utilizes information about the relationships among sentences in bug reports, [PERSON] et al. [5] applied _sentence believability_ to summarize bug reports. They considered that the discussions in bug reports are cross-validated among different developers who express their own opinions (i.e., evaluation behaviors) to evaluate others' comments. Comments in a bug report are either supported or disapproved by others. [PERSON] et al. [5] showed that using believability scores can improve the quality of bug report summaries; however, for bug reports that do not contain such evaluated sentences, it is difficult to assign believability scores to sentences; therefore, the effect of their method is limited to the bug reports that contain evaluated sentences. To measure the importance of all sentences, [PERSON] et al. [10] combined sentence believability score [5] with the TextRank-based text ranking score [11]; however, their approach is limited to the combination of two existing methods [5; 11].  
In this paper, we propose a deep-learning-based bug report summarization method that utilizes the notion of sentence significance factors, where a _sentence significance factor_ is a criterion for identifying important sentences. The main idea behind our proposed  
Figure 1: Example of a bug report.  
method is that users can obtain high-quality summaries without limiting the applicability by determining a suitable set of sentence significance factors and their weights for the target bug report.  
We evaluate the proposed method with two datasets using three sentence significance factors: sentence-to-sentence cohesion, topic association, and believability. The experimental results show that our proposed method outperforms the state-of-the-art method of BugSum [5] with respect to precision, recall, and F-score and that the application scope of the proposed method is wider than that of BugSum [5].  
The contributions of this paper are presented as follow: First, we proposed a comprehensive deep-learning-based bug report summarization method that encompasses diverse sentence significance factors. Second, we compared the proposed method with the state-of-the-art method BugSum with two benchmark datasets and showed that our comprehensive method that uses three sentence significance factors yields a better result than the result of BugSum and that our method works even when BugSum does not work.  
This paper is organized as follows: Section 2 introduces existing studies related to bug report summarization. Section 3 describes our proposed method. Sections 4 and 5 evaluate the proposed method. Section 6 discusses threats to validity and implications of our study. Section 7 concludes our work and discusses future research challenges.