Fetch user-specific research papers from arXiv on a daily schedule, process and structure the data, and create or update entries in a Notion database, with support for data delivery Paper Topic: single query keyword Update Frequency: Daily updates, with fewer than 20 entries expected per day Tools: Platform: n8n, for end-to-end workflow configuration AI Model: Gemini-2.5-Flash, for daily paper summarization and data processing Database: Notion, with two tables — Daily Paper Summary and Paper Details Message: Feishu (IM bot notifications), Gmail (email notifications) Data Retrieval arXiv API The arXiv provides a public API that allows users to query research papers by topic or by predefined categories. arXiv API User Manual Key Notes: Response Format: The API returns data as a typical Atom Response. Timezone & Update Frequency: The arXiv submission process operates on a 24-hour cycle. Newly submitted articles become available in the API only at midnight after they have been processed. Feeds are updated daily at midnight Eastern Standard Time (EST). Therefore, a single request per day is sufficient. Request Limits: The maximum number of results per call (max_results) is 30,000, Results must be retrieved in slices of at most 2,000 at a time, using the max_results and start query parameters. Time Format: The expected format is [YYYYMMDDTTTT+TO+YYYYMMDDTTTT], TTTT is provided in 24-hour time to the minute, in GMT. Scheduled Task Execution Frequency: Daily Execution Time: 6:00 AM Time Parameter Handling (JS): According to arXiv’s update rules, the scheduled task should query the previous day’s (T-1) submittedDate data. Data Extraction Data Cleaning Rules (Convert to Standard JSON) Remove Header Keep only the 【entry】【/entry】 blocks representing paper items. Single Item Each 【entry】【/entry】 represents a single item. Field Processing Rules 【id】【/id】 ➡️ id Extract content. Example: 【id】http://arxiv.org/abs/2409.06062v1【/id】 → http://arxiv.org/abs/2409.06062v1 【updated】【/updated】 ➡️ updated Convert timestamp to yyyy-mm-dd hh:mm:ss 【published】【/published】 ➡️ published Convert timestamp to yyyy-mm-dd hh:mm:ss 【title】【/title】 ➡️ title Extract text content 【summary】【/summary】 ➡️ summary Keep text, remove line breaks 【author】【/author】 ➡️ author Combine all authors into an array Example: [ "Ernest Pusateri", "Anmol Walia" ] (for Notion multi-select field) 【arxiv:comment】【/arxiv:comment】 ➡️ Ignore / discard 【link type="text/html"】 ➡️ html_url Extract URL 【link type="application/pdf"】 ➡️ pdf_url Extract URL 【arxiv:primarycategory term="cs.CL"】 ➡️ primarycategory Extract term value 【category】 ➡️ category Merge all 【category】 values into an array Example: [ "eess.AS", "cs.SD" ] (for Notion multi-select field) Add Empty Fields github huggingface Data Processing Analyze and summarize paper data using AI, then standardize output as JSON. Single Paper Basic Information Analysis and Enhancement Daily Paper Summary and Multilingual Translation Data Storage: Notion Database Create a corresponding database in Notion with the same predefined field names. In Notion, create an integration under Integrations and grant access to the database. Obtain the corresponding Secret Key. Use the Notion "Create a database page" node to configure the field mapping and store the data. Notes "Create a database page" only adds new entries; data will not be updated. The updated and published timestamps of arXiv papers are in UTC. Notion single-select and multi-select fields only accept arrays. They do not automatically parse comma-separated strings. You need to format them as proper arrays. Notion does not accept null values, which causes a 400 error. Data Delivery Set up two channels for message delivery: EMAIL and IM, and define the message format and content. Email: Gmail GMAIL OAuth 2.0 – Official Documentation Configure your OAuth consent screen Steps: Enable Gmail API Create OAuth consent screen Create OAuth client credentials Audience: Add Test users under Testing status Message format: HTML (Model: OpenAI GPT — used to design an HTML email template) IM: Feishu (LARK) Bots in groups Use bots in groups
