A Survey Forest Diagram : Gain a Divergent Insight View on a Specific Research Topic
Jinghong Li, Wen Gu, Koichi Ota, Shinobu Hasegawa
TL;DR
This paper addresses the challenge that novice researchers face when using Generative AI for literature surveys, specifically their ability to derive divergent insights from citations. It introduces the Divergent Insight Survey and the Survey Forest Diagram, a layered, citation-clue–driven representation that organizes survey papers and the papers they cite by citation intention, motivation, and clues, enabling multi-directional exploration. The methodology leverages a prompt-engineering pipeline to extract an initial prototype from the HotpotQA topic in the S2orc dataset, locate precise citation sentences, and generate abstractive summaries highlighting citation clues and linking them to research objectives. The work provides a conceptual framework and an actionable visualization strategy for accelerated, diversified survey reasoning, with planned improvements including refined prompts, adjustable insight ranges, and summary-length controls.
Abstract
With the exponential growth in the number of papers and the trend of AI research, the use of Generative AI for information retrieval and question-answering has become popular for conducting research surveys. However, novice researchers unfamiliar with a particular field may not significantly improve their efficiency in interacting with Generative AI because they have not developed divergent thinking in that field. This study aims to develop an in-depth Survey Forest Diagram that guides novice researchers in divergent thinking about the research topic by indicating the citation clues among multiple papers, to help expand the survey perspective for novice researchers.