The text discusses the challenges of teaching computers to understand human language, specifically in the context of customer support conversations. It highlights the limitations of simply identifying the most common sentences and proposes using document embeddings as a solution. Document embeddings are used to translate documents into vectors that can be processed by computers, allowing for automated analysis and clustering of similar meanings. The text then describes how BERTopic, an open-source software package, is used to group similar documents together based on their meaning, providing insights into frequently asked questions and pain points in customer support conversations. The results show the ability to identify 65 clusters with varying frequencies, representative documents, and topics over time, offering a new perspective on understanding customer needs and improving product development.