ConSum4DCR: A Q&A Knowledge Extraction Method for Developer Chatroom Environments

This paper presents a Q&A knowledge extraction method for developer chatroom environments by summarizing their conversations — ConSum4DCR. In the era of AI, the Internet of Things, and cyber-physical systems, software runtime environments have become increasingly diverse and rapidly evolving. As a critical component of collaborative software development, developer chatrooms exhibit dynamic and multifaceted characteristics (e.g., Q&A patterns in technical discussions, integration with external services such as code snippets or links), which drive developers’ demand for efficient information retrieval. To address the unique nature of conversation texts in developer chatrooms, ConSum4DCR employs an extractive text summarization approach. It segments content into paragraphs at the conversation thread level, applies a topic disentanglement algorithm to handle topic shifts, and uses heuristic rules to prune and merge conversation content to form concise conversation summaries. The paper also proposes experiments to evaluate the effectiveness and readability of the summaries. It assesses summary quality based on the performance of automatic text classifiers and engages developers to read the summaries and complete classification tasks, verifying the extent to which the summaries preserve environmental semantics and enhance comprehensibility. Results show that the method performs excellently in terms of both effectiveness and readability.