## 2 Related Work  
Text summarization techniques can be classified into the abstractive approach and the extractive approach. Abstractive summarization generates a summary by using new phrases and sentences that may not appear in the original text. Extractive summarization organizes a summary by extracting a paragraph or sentence from the original text. Bug report summarization has been extensively studied in the direction of extractive summarization. The extractive summarization approach is highly relevant to this paper and is discussed in detail in the remainder of this section. [PERSON] et al. [6] proposed a bug report summarization method based on a supervised learning technique. They applied minutes and email tread summarization methods to bug reports, considering that bug reports are typically written in a dialog format. [PERSON] et al. [6] prepared a handwritten summary of 36 bug reports for training and evaluation. [PERSON] et al. [7] confirmed that duplicated bug reports have textual similarity with original bug reports, and based on this relationship, proposed a bug report summarization method using the PageRank algorithm. They combined the method of [PERSON] et al. [6] with the PageRank-based summarization method to improve the quality of bug report summaries. Additionally, they created new OSCAR data containing duplicated bug reports. [PERSON] et al. [6] and [PERSON] et al. [7] trained the summarizer using the summaries that they created; however, in these studies, the quality of a summary is heavily dependent on training data, which requires annotators to exert massive manual efforts to create the training data. Although many supervised-learning-based methods have been adopted, unsupervised-learning-based methods have been employed to avoid massive efforts for manually label data. [PERSON] et al. [8] proposed a graph-based, unsupervised, summarization method that mimics human reading behaviors. They simulated human reading patterns to score sentences, connected the sentences according to similarities, and chose the sentences with the highest possibilities of being reached while randomly moving between two sentences. [PERSON] et al. [9] proposed an unsupervised, learning-based, summarization method using deep learning. They focused on predefined words and sentence types and scored sentences based on the weights of words; however, existing unsupervised approaches are sensitive to word frequency [4; 9] and are likely to include redundant sentences that represent a similar topic in the summary, which would produce biased results. [PERSON] et al. [5] proposed a deep-learning-based, unsupervised, summarization method that measures the sentence believability score. They considered that bug reports contain evaluation behaviors, which are positive or negative opinions in the form of comments. They defined a sentence that evaluates a specific sentence as an _evaluator sentence_ and a specific sentence that is evaluated by other sentences as an _evaluated sentence_. They assigned positive or negative weights to the _evaluated sentences_ by other _evaluator sentences_. Based on the weights, they computed believability scores and improved summarization performance. [PERSON] et al. [5] found that 28.5% of the 31,155 bug reports do not contain evaluation behaviors. In addition, in the case of bug reports with evaluation behaviors, the evaluated sentences comprised only 36.4% of the sentences on average. Thus, for bug reports that do not contain evaluation behaviors or bug reports that contain them only for a few sentences, their method will not work well. [PERSON] [12] summarized the document using many features at the sentence-based level, such as the sentence length, sentence position, sentence-to-sentence cohesion, and similarity to title. For example, similarity to title is calculated by comparing all the sentences of the document against the title of the document.