@inproceedings{42010b9b5c5e4b8bba24e70f22a91e66,
title = "TextData: Save What You Know and Find What You Don't",
abstract = "In this demonstration, we present TextData, a novel online system that enables users to both {"}save what they know{"}and {"}find what they don't{"}. TextData was developed based on the Community Digital Library (CDL) system. Although the CDL allowed users to bookmark webpages with plain text and provided search and recommendation, it fell short in key features. To better help users save what they know, TextData offers the addition of markdown to submissions for providing a richer method of note-taking. To better help users find what they don't, TextData provides methods for visualizing the relationships among submissions and provides in-context interactive search intent prediction with question-answering via a generative large language model. TextData is free-to-use, can be accessed online, and the source code is publicly available.",
keywords = "in-context search, information retrieval, interactive search, note-taking, question answering, recommendation, social bookmarking",
author = "Kevin Ros and Kedar Takwane and Ashwin Patil and Rakshana Jayaprakash and Zhai, {Cheng Xiang}",
note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024 ; Conference date: 14-07-2024 Through 18-07-2024",
year = "2024",
month = jul,
day = "10",
doi = "10.1145/3626772.3657681",
language = "English (US)",
series = "SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery",
pages = "2806--2810",
booktitle = "SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval",
address = "United States",
}