Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking

TL;DR

Synergi is a mixed-initiative system that combines user input, citation graphs, and large language models to help scholars synthesize and explore research threads more efficiently and interactively.

Contribution

It introduces a novel computational pipeline that enables iterative, personalized scholarly synthesis by integrating user-guided seed threads with automated expansion and structuring.

Findings

Enhances scholars' ability to understand research threads

Broadens perspectives and increases curiosity

Facilitates efficient synthesis of complex scholarly information

Abstract

Efficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever. While significant research has been devoted to helping scholars interact with individual papers, building research threads scattered across multiple papers remains a challenge. Most top-down synthesis (and LLMs) make it difficult to personalize and iterate on the output, while bottom-up synthesis is costly in time and effort. Here, we explore a new design space of mixed-initiative workflows. In doing so we develop a novel computational pipeline, Synergi, that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively. Synergi allows scholars to start with an entire threads-and-subthreads structure generated from papers relevant to their interests, and to iterate and customize on it as they wish. In our evaluation, we find that Synergi helps scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. We discuss future design implications for thread-based, mixed-initiative scholarly synthesis support tools.