AI-Augmented Reading Interfaces

TLDRCiteSee is a paper reading tool that leverages a user’s publishing, reading, and saving activities to provide personalized visual augmentations and context around citations to help users prioritize their exploration. Expand

AbstractDespite growing interest in applying natural language processing (NLP) and computer vision (CV) models to the scholarly domain, scientific documents remain challenging to work with. They’re often in difficult-to-use PDF formats, and the ecosystem of ... Expand

TLDRIt is demonstrated that participants effectively used IdeaSynth for real-world research projects at various ideation stages from developing initial ideas to revising framings of mature manuscripts, highlighting the possibilities to adopt IdeaSynth in researcher’s workflows. Expand

TLDRIt was shown that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers. Expand

TLDRA novel computational pipeline is developed that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively, and is found to help scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. Expand

TLDRRecursively expandable abstracts are introduced, a novel interaction paradigm that dynamically expands abstracts by progressively incorporating additional information from the papers’ full text during their dive into the full text. Expand

TLDRThe Semantic Reader Project is described, a collaborative effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers, and a collection of novel reading interfaces are developed and evaluated and evaluated them with study participants and real-world users to show improved reading experiences for scholars. Expand

TLDRPapeos is presented, a novel reading and authoring interface that allow authors to augment their papers by segmenting and localizing talk videos alongside relevant paper passages with automatically generated suggestions. Expand

TLDRThis work designs 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. Expand

TLDRThis research investigates features representing domain, subdomain, and individual knowledge to predict individual jargon familiarity and compares supervised and prompt-based approaches, finding that prompt-based methods yield the highest accuracy, though the task remains difficult and supervised approaches have lower false positive rates. Expand

TLDRA tool integrated into users’ reading process that helps them with leveraging authors’ existing summarization of threads, typically in introduction or related work sections, in order to situate their own work’s contributions is developed. Expand