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As developers, we often deal with large volumes of text, and making sense of it can be a challenge. In many cases, we might only be interested in a summary of the text or a quick overview of its main points. This is where text summarization comes in.
Text summarization is the process of automatically creating a shorter version of a text that preserves its key information. It has many applications in natural language processing (NLP), from summarizing news articles to generating abstracts for scientific papers. Even products, including Notion, are integrating AI features that will summarize a block of text on command.
One interesting use case is summarizing chat conversations, where the goal is to distill the main topics and ideas discussed during the conversation. That’s what we are going to explore in this article. Whether you’re an experienced developer or just getting started with natural language processing, this article will provide a practical guide to building a chat summarizer from scratch. By the end, you’ll have a working chat summarizer that you can use to extract the main ideas from your own chat conversations — or any other text data that you might encounter in your projects.