From Data Dump to Digestible Chunks: Automated Segmentation and Summarization of Provenance Logs for Communication
<|reference_start|>From Data Dump to Digestible Chunks: Automated Segmentation and Summarization of Provenance Logs for Communication: Communicating one's sensemaking during a complex analysis session to explain thought processes is hard, yet most intelligence occurs in collaborative settings. Team members require a deeper understanding of the work being completed by their peers and subordinates, but little research has fully articulated best practices for analytic provenance consumers. This work proposes an automatic summarization technique that separates an analysis session and summarizes interaction provenance as textual blurbs to allow for meta-analysis of work done. Focusing on the domain of intelligence analysis, we demonstrate our segmentation technique using five datasets, including both publicly available and classified interaction logs. We shared our demonstration with a notoriously inaccessible population of expert reviewers with experience as United States Department of Defense analysts. Our findings indicate that the proposed pipeline effectively generates cards that display key events from interaction logs, facilitating the sharing of analysis progress. Yet, we also hear that there is a need for more prominent justifications and pattern elicitation controls to communicate analysis summaries more effectively. The expert review highlights the potential of automated approaches in addressing the challenges of provenance information in complex domains. We'd like to emphasize the need for further research into provenance communication in other domains.   A free copy of this paper and all supplemental materials are available at https://osf.io/j4bxt<|reference_end|>
@article{block2024from,
    title={From Data Dump to Digestible Chunks: Automated Segmentation and
  Summarization of Provenance Logs for Communication},
    author={Jeremy E. Block, Donald Honeycutt, Brett Benda, Benjamin Rheault, Eric
  D. Ragan},
    journal={arXiv preprint arXiv:2409.04616},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04616},
    primaryClass={cs.HC}
}