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Every day, we crawl new papers published on major academic paper websites (like arxiv, medrxiv, pubmed) as well as hundreds of conferences/journals, and then generate a one sentence summary for each paper to capture the paper highlight. We are eager to share the results with our subscribers on a daily basis!
Sign up for the Daily Paper Digest to get the daily paper update straight to your inbox. You can also choose not to receive emails and instead read the daily updates online.
Our daily paper digest service started in May, 2018, and has been very well received in the community. Currently, more than 200 areas under the following subjects are tracked: Biology, Computer Science, EE and System Science, Finance, Health Science, Math, Physics, and Statistics.
1 Minute to Sign Up
Click on “Get Started” button.
Tell us your email and interested areas.
Add Payment info, and click on “Submit” to finish.
Advanced Setting: Filter by Author and Keyword
Our default setting is to send you all papers under the categories you signed up with. Many users can browse hundreds of papers everyday using the one-sentence highlight.
If you do not have time to browse all papers, you can either filter papers using keywords on our console, or update the tracking list by including some authors and keywords.
When time is limited, users can choose to read the papers that can pass the filters. Such papers are marked with * in email and put in a separate list (called tracking results) online.
John Smith signed up with 6 categories: cs.AI; cs.CL; cs.CV; cs.IR; cs.LG; cs.SI, and receive >100 papers under them. He also added two authors and two keywords to tracking list: Michael I. Jordan ; Huan Liu ; multi-task learning ; reinforcement learning’ and receive only ~10 papers in the tracking results.
Highlights like the following will be included in daily emails as part of the tracking result
(Mavi et. al., 2022) aim to provide a general and formal definition of MHQA task, and organize and summarize existing MHQA frameworks. (Xia et. al., 2022) present a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely “LingYi”, which is designed as a pipeline framework to maintain high flexibility. Neural passage retrieval is a new and promising approach in open retrieval question answering. (Reddy et. al., 2022) find that it lags behind standard BM25 in this important real-world setting. (Lipping et. al., 2022) introduce Clotho-AQA, a dataset for Audio question answering consisting of 1991 audio files each between 15 to 30 seconds in duration selected from the Clotho dataset .