Hey fellow Lemmings,

I’m thrilled to announce the launch of AI News Summary Bot, a project that brings you News summary! The bot is now live on our community at !news_summary@lemmy.dbzer0.com.

The bot is still in its early stages, and I’m excited to hear your feedback and suggestions on how to improve it. Feel free to share your thoughts and ideas.

Repository: If you’re interested in contributing or exploring the code behind the bot, you can find the repository at https://github.com/muntedcrocodile/ai_news_bot.

Donations: If you’re interested in donating to allow me to spend more time developing please do: monero:8916FjDhEqXJqX9Koec9WaZ4QBQAa6sgW6XhQhXSjYWpQiWB42GsggEh73YAFGF86GU2gEE1TTRdWSspuMgpWGkiPHkgBTX

Stay informed, and let’s build this community together!

EDIT: grammar

    • Admiral Patrick@dubvee.orgEnglish
      163·
      3 days ago

      The concept is inherently flawed when you introduce an aspect (LLM) that can and will hallucinate (read: make shit up) when it’s trying to present reality.

      As far as I’m concerned, there is no place for that anywhere remotely close to news.

      • Staden_ スタデン@pawb.socialEnglish
        41·
        3 days ago

        correct, but humans also exaggerate and lie a lot in the news, so maybe this AI could look through different sources and identify inaccuracies.

        I haven’t looked the source code tho…

        • Staden_ スタデン@pawb.socialEnglish
          3·
          3 days ago

          After checking the source code, well… it just summarizes the posts. Doesn’t help much with the human error problem.

          But as mentioned by OP, it’s in early stage of development, and they plan to add features to “find the missing perspectives on an issue” and analyze political alligmnent information. So in the future maybe it could become a useful tool.

      • 🇦🇺𝕄𝕦𝕟𝕥𝕖𝕕𝕔𝕣𝕠𝕔𝕕𝕚𝕝𝕖@lemm.eeOPEnglish
        32·
        3 days ago

        The model i have used gives a 60% identical summary to that provided by a human. And has an overall conceptual accuracy of >95% i was very carefull with my model selection and implementation as to ensure hallucinations are extremely rare if at all possible. Im not just feeding in “summarise this: <text>” to a general purpose llm (known for hallucinations) i break the article into chunks at sentence breaks then make a summary of that chunk directly by passing it to a purpose build summarisation model.