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Automated Summarization of Stack Overflow Posts.

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Title: Automated Summarization of Stack Overflow Posts.
Authors: Kou, Bonan1 koub@purdue.edu, Chen, Muhao2 muhaoche@usc.edu, Zhang, Tianyi1 tianyi@purdue.edu
Source: ICSE: International Conference on Software Engineering. 2023, p1853-1865. 13p.
Subjects: Stack Overflow (Company), Computer software, Deep learning, Natural language processing, Heuristic algorithms
Abstract: Software developers often resort to Stack Overflow (SO) to fill their programming needs. Given the abundance of relevant posts, navigating them and comparing different solutions is tedious and time-consuming. Recent work has proposed to automatically summarize SO posts to concise text to facilitate the navigation of SO posts. However, these techniques rely only on information retrieval methods or heuristics for text summarization, which is insufficient to handle the ambiguity and sophistication of natural language. This paper presents a deep learning based framework called Assort for SO post summarization. Assort includes two complementary learning methods, AssortS and AssortIS, to address the lack of labeled training data for SO post summarization. AssortS is designed to directly train a novel ensemble learning model with BERT embeddings and domain-specific features to account for the unique characteristics of SO posts. By contrast, AssortIS is designed to reuse pre-trained models while addressing the domain shift challenge when no training data is present (i.e., zero-shot learning). Both AssortS and AssortIS outperform six existing techniques by at least 13% and 7% respectively in terms of the F1 score. Furthermore, a human study shows that participants significantly preferred summaries generated by AssortS and AssortIS over the best baseline, while the preference difference between AssortS and AssortIS was small. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Automated Summarization of Stack Overflow Posts.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Kou%2C+Bonan%22">Kou, Bonan</searchLink><relatesTo>1</relatesTo><i> koub@purdue.edu</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Muhao%22">Chen, Muhao</searchLink><relatesTo>2</relatesTo><i> muhaoche@usc.edu</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Tianyi%22">Zhang, Tianyi</searchLink><relatesTo>1</relatesTo><i> tianyi@purdue.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2023, p1853-1865. 13p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Stack+Overflow+%28Company%29%22">Stack Overflow (Company)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Software developers often resort to Stack Overflow (SO) to fill their programming needs. Given the abundance of relevant posts, navigating them and comparing different solutions is tedious and time-consuming. Recent work has proposed to automatically summarize SO posts to concise text to facilitate the navigation of SO posts. However, these techniques rely only on information retrieval methods or heuristics for text summarization, which is insufficient to handle the ambiguity and sophistication of natural language. This paper presents a deep learning based framework called Assort for SO post summarization. Assort includes two complementary learning methods, AssortS and AssortIS, to address the lack of labeled training data for SO post summarization. AssortS is designed to directly train a novel ensemble learning model with BERT embeddings and domain-specific features to account for the unique characteristics of SO posts. By contrast, AssortIS is designed to reuse pre-trained models while addressing the domain shift challenge when no training data is present (i.e., zero-shot learning). Both AssortS and AssortIS outperform six existing techniques by at least 13% and 7% respectively in terms of the F1 score. Furthermore, a human study shows that participants significantly preferred summaries generated by AssortS and AssortIS over the best baseline, while the preference difference between AssortS and AssortIS was small. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/ICSE48619.2023.00158
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 1853
    Subjects:
      – SubjectFull: Stack Overflow (Company)
        Type: general
      – SubjectFull: Computer software
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Heuristic algorithms
        Type: general
    Titles:
      – TitleFull: Automated Summarization of Stack Overflow Posts.
        Type: main
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            NameFull: Kou, Bonan
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            NameFull: Chen, Muhao
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            NameFull: Zhang, Tianyi
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          Dates:
            – D: 01
              M: 05
              Text: 2023
              Type: published
              Y: 2023
          Titles:
            – TitleFull: ICSE: International Conference on Software Engineering
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