96 research outputs found
Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking
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.Comment: ACM UIST'2
ComLittee: Literature Discovery with Personal Elected Author Committees
In order to help scholars understand and follow a research topic, significant
research has been devoted to creating systems that help scholars discover
relevant papers and authors. Recent approaches have shown the usefulness of
highlighting relevant authors while scholars engage in paper discovery.
However, these systems do not capture and utilize users' evolving knowledge of
authors. We reflect on the design space and introduce ComLittee, a literature
discovery system that supports author-centric exploration. In contrast to
paper-centric interaction in prior systems, ComLittee's author-centric
interaction supports curation of research threads from individual authors,
finding new authors and papers with combined signals from a paper recommender
and the curated authors' authorship graphs, and understanding them in the
context of those signals. In a within-subjects experiment that compares to an
author-highlighting approach, we demonstrate how ComLittee leads to a higher
efficiency, quality, and novelty in author discovery that also improves paper
discovery
Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections
Scholars who want to research a scientific topic must take time to read,
extract meaning, and identify connections across many papers. As scientific
literature grows, this becomes increasingly challenging. Meanwhile, authors
summarize prior research in papers' related work sections, though this is
scoped to support a single paper. A formative study found that while reading
multiple related work paragraphs helps overview a topic, it is hard to navigate
overlapping and diverging references and research foci. In this work, we design
a system, Relatedly, that scaffolds exploring and reading multiple related work
paragraphs on a topic, with features including dynamic re-ranking and
highlighting to spotlight unexplored dissimilar information, auto-generated
descriptive paragraph headings, and low-lighting of redundant information. From
a within-subjects user study (n=15), we found that scholars generate more
coherent, insightful, and comprehensive topic outlines using Relatedly compared
to a baseline paper list
CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context
When reading a scholarly article, inline citations help researchers
contextualize the current article and discover relevant prior work. However, it
can be challenging to prioritize and make sense of the hundreds of citations
encountered during literature reviews. This paper introduces CiteSee, a paper
reading tool that leverages a user's publishing, reading, and saving activities
to provide personalized visual augmentations and context around citations.
First, CiteSee connects the current paper to familiar contexts by surfacing
known citations a user had cited or opened. Second, CiteSee helps users
prioritize their exploration by highlighting relevant but unknown citations
based on saving and reading history. We conducted a lab study that suggests
CiteSee is significantly more effective for paper discovery than three
baselines. A field deployment study shows CiteSee helps participants keep track
of their explorations and leads to better situational awareness and increased
paper discovery via inline citation when conducting real-world literature
reviews
Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks
Large language models have introduced exciting new opportunities and
challenges in designing and developing new AI-assisted writing support tools.
Recent work has shown that leveraging this new technology can transform writing
in many scenarios such as ideation during creative writing, editing support,
and summarization. However, AI-supported expository writing--including
real-world tasks like scholars writing literature reviews or doctors writing
progress notes--is relatively understudied. In this position paper, we argue
that developing AI supports for expository writing has unique and exciting
research challenges and can lead to high real-world impacts. We characterize
expository writing as evidence-based and knowledge-generating: it contains
summaries of external documents as well as new information or knowledge. It can
be seen as the product of authors' sensemaking process over a set of source
documents, and the interplay between reading, reflection, and writing opens up
new opportunities for designing AI support. We sketch three components for AI
support design and discuss considerations for future research.Comment: 3 pages, 1 figure, accepted by The Second Workshop on Intelligent and
Interactive Writing Assistant
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Scholarly publications are key to the transfer of knowledge from scholars to
others. However, research papers are information-dense, and as the volume of
the scientific literature grows, the need for new technology to support the
reading process grows. In contrast to the process of finding papers, which has
been transformed by Internet technology, the experience of reading research
papers has changed little in decades. The PDF format for sharing research
papers is widely used due to its portability, but it has significant downsides
including: static content, poor accessibility for low-vision readers, and
difficulty reading on mobile devices. This paper explores the question "Can
recent advances in AI and HCI power intelligent, interactive, and accessible
reading interfaces -- even for legacy PDFs?" We describe the Semantic Reader
Project, a collaborative effort across multiple institutions to explore
automatic creation of dynamic reading interfaces for research papers. Through
this project, we've developed ten research prototype interfaces and conducted
usability studies with more than 300 participants and real-world users showing
improved reading experiences for scholars. We've also released a production
reading interface for research papers that will incorporate the best features
as they mature. We structure this paper around challenges scholars and the
public face when reading research papers -- Discovery, Efficiency,
Comprehension, Synthesis, and Accessibility -- and present an overview of our
progress and remaining open challenges
Federated learning enables big data for rare cancer boundary detection.
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing
Author Correction: Federated learning enables big data for rare cancer boundary detection.
10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14
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