But he did point out that the firm can leverage its speech-to-text knowledge to focus on more intense debates taking place within a longer discussion. And unsurprisingly, Cannings wasn’t about to share any company IP with TechHQ. Intelligent Voice has its own secret sauce for chopping up meetings into digestible elements. And given that 100 tokens (the elements used to represent different parts of the user prompt and AI-generated output) equate to around 75 words, the LLM could potentially struggle with meeting transcriptions that run into many thousands of words. GPT-3.5, which formed the basis of ChatGPT at launch, had a context length of 4000 tokens.
But ChatGPT’s context length – the length of the prompt plus the maximum number of tokens in the completion – is relatively short (or at least it used to be). Online tools such as OpenAI’s Research Preview of ChatGPT make it straightforward for users to put LLMs to work to generate a synopsis of a longer document. “And the style is influenced by what was in the original material.” “It’s written from scratch based on the data that’s been given to it,” Nigel Cannings, CTO of Intelligent Voice, told TechHQ. Some of the phrases may be similar to those in the transcription, but abstractive summarization is much more than just copy and pasting from the input data. Video conferencing services upgrade business prospectsĪbstractive summarization leverages deep-learning methods to convey a sense of what is being said and puts LLMs to work to condense pages of text into a quick-reading executive summary. AI-generated meeting summaries can distill long meeting discussions into key action points, and there are a couple of approaches that developers can deploy to achieve that. But as with any long document, when the time window is short, it’s helpful to have an executive summary that puts the essential details up front. And, today, the accuracy of meeting transcriptions has never been higher. Online platforms such as Zoom, Teams, and other meetings providers have made great strides in speech-to-text conversion. And with online meetings commonplace, connecting remote workers or catering for hybrid on-premises and at-home scenarios, there’s a wealth of productivity gains up for grabs, including AI-generated meeting summaries.
Machine learning tools have long been able to generate calendar entries based on key phrases in emails or instant messages. And adding to a fast-growing list of applications for large language models (LLMs) – such as the newly updated GPT-4, which is being rolled out across OpenAI’s suite of services – is meeting summarization. Enterprise uses for artificial intelligence (AI) are on the increase.
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