Summarizing Books as Podcasts – O’Reilly

Like nearly everybody, we have been impressed by the flexibility of NotebookLM to generate podcasts: Two digital folks holding a dialogue. You can provide it some hyperlinks, and it’ll generate a podcast based mostly on the hyperlinks. The podcasts have been fascinating and interesting. However in addition they had some limitations.

The issue with NotebookLM is that, whilst you can provide it a immediate, it largely does what it’s going to do. It generates a podcast with two voices—one male, one feminine—and provides you little management over the outcome. There’s an elective immediate to customise the dialog, however that single immediate doesn’t will let you do a lot. Particularly, you may’t inform it which matters to debate or in what order to debate them. You may strive, nevertheless it received’t hear. It additionally isn’t conversational, which is one thing of a shock now that we’ve all gotten used to chatting with AIs. You may’t inform it to iterate by saying “That was good, however please generate a brand new model altering these particulars” like you may with ChatGPT or Gemini.

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Can we do higher? Can we combine our data of books and know-how with AI’s skill to summarize? We’ve argued (and can proceed to argue) that merely studying the way to use AI isn’t sufficient; you want to learn to do one thing with AI that’s higher than what the AI may do by itself. You want to combine synthetic intelligence with human intelligence. To see what that may seem like in observe, we constructed our personal toolchain that provides us rather more management over the outcomes. It’s a multistage pipeline:

• We use AI to generate a abstract for every chapter of a ebook, ensuring that each one the essential matters are lined.
• We use AI to assemble the chapter summaries right into a single abstract. This step basically provides us an prolonged define.
• We use AI to generate a two-person dialogue that turns into the podcast script.
• We edit the script by hand, once more ensuring that the summaries cowl the suitable matters in the suitable order. That is additionally a possibility to appropriate errors and hallucinations.
• We use Google’s speech-to-text multispeaker API (nonetheless in preview) to generate a abstract podcast with two individuals.

Why are we specializing in summaries? Summaries curiosity us for a number of causes. First, let’s face it: Having two nonexistent folks talk about one thing you wrote is fascinating—particularly since they sound genuinely and excited. Listening to the voices of nonexistent cyberpeople talk about your work makes you are feeling such as you’re residing in a sci-fi fantasy. Extra virtually: Generative AI is certainly good at summarization. There are few errors and virtually no outright hallucinations. Lastly, our customers need summarization. On O’Reilly Solutions, our prospects steadily ask for summaries: summarize this ebook, summarize this chapter. They need to discover the knowledge they want. They need to discover out whether or not they really want to learn the ebook—and if that’s the case, what components. A abstract helps them try this whereas saving time. It lets them uncover shortly whether or not the ebook will probably be useful, and does so higher than the again cowl copy or a blurb on Amazon.

With that in thoughts, we needed to assume by what probably the most helpful abstract could be for our members. Ought to there be a single speaker or two? When a single synthesized voice summarized the ebook, my eyes (ears?) glazed over shortly. It was a lot simpler to take heed to a podcast-style abstract the place the digital individuals have been excited and enthusiastic, like those on NotebookLM, than to a lecture. The give and take of a dialogue, even when simulated, gave the podcasts power {that a} single speaker didn’t have.

How lengthy ought to the abstract be? That’s an essential query. In some unspecified time in the future, the listener loses curiosity. We may feed a ebook’s whole textual content right into a speech synthesis mannequin and get an audio model—we might but try this; it’s a product some folks need. However on the entire, we count on summaries to be minutes lengthy somewhat than hours. I would hear for 10 minutes, perhaps 30 if it’s a subject or a speaker that I discover fascinating. However I’m notably impatient after I take heed to podcasts, and I don’t have a commute or different downtime for listening. Your preferences and your state of affairs could also be a lot completely different.

What precisely do listeners count on from these podcasts? Do customers count on to study, or do they solely need to discover out whether or not the ebook has what they’re in search of? That depends upon the subject. I can’t see somebody studying Go from a abstract—perhaps extra to the purpose, I don’t see somebody who’s fluent in Go studying the way to program with AI. Summaries are helpful for presenting the important thing concepts offered within the ebook: For instance, the summaries of Cloud Native Go gave a very good overview of how Go may very well be used to deal with the problems confronted by folks writing software program that runs within the cloud. However actually studying this materials requires examples, writing code, and training—one thing that’s out of bounds in a medium that’s restricted to audio. I’ve heard AIs learn out supply code listings in Python; it’s terrible and ineffective. Studying is extra possible with a ebook like Facilitating Software program Structure, which is extra about ideas and concepts than code. Somebody may come away from the dialogue with some helpful concepts and presumably put them into observe. However once more, the podcast abstract is simply an outline. To get all the worth and element, you want the ebook. In a latest article, Ethan Mollick writes, “Asking for a abstract is just not the identical as studying for your self. Asking AI to resolve an issue for you is just not an efficient solution to study, even when it feels prefer it needs to be. To study one thing new, you’ll should do the studying and pondering your self.”

One other distinction between the NotebookLM podcasts and ours could also be extra essential. The podcasts we generated from our toolchain are all about six minutes lengthy. The podcasts generated by NotebookLM are within the 10- to 25-minute vary. The longer size may permit the NotebookLM podcasts to be extra detailed, however in actuality that’s not what occurs. Somewhat than discussing the ebook itself, NotebookLM tends to make use of the ebook as a leaping off level for a broader dialogue. The O’Reilly-generated podcasts are extra directed. They observe the ebook’s construction as a result of we supplied a plan, a top level view, for the AI to observe. The digital podcasters nonetheless categorical enthusiasm, nonetheless herald concepts from different sources, however they’re headed in a course. The longer NotebookLM podcasts, in distinction, can appear aimless, looping again round to choose up concepts they’ve already lined. To me, not less than, that appears like an essential level. Granted, utilizing the ebook because the jumping-off level for a broader dialogue can also be helpful, and there’s a steadiness that must be maintained. You don’t need it to really feel such as you’re listening to the desk of contents. However you additionally don’t need it to really feel unfocused. And if you’d like a dialogue of a ebook, you must get a dialogue of the ebook.

None of those AI-generated podcasts are with out limitations. An AI-generated abstract isn’t good at detecting and reflecting on nuances within the unique writing. With NotebookLM, that clearly wasn’t underneath our management. With our personal toolchain, we may definitely edit the script to replicate no matter we wished, however the voices themselves weren’t underneath our management and wouldn’t essentially observe the textual content’s lead. (It’s controversial that reflecting the nuances of a 250-page ebook in a six-minute podcast is a shedding proposition.) Bias—a sort of implied nuance—is a much bigger situation. Our first experiments with NotebookLM tended to have the feminine voice asking the questions, with the male voice offering the solutions, although that appeared to enhance over time. Our toolchain gave us management, as a result of we supplied the script. We received’t declare that we have been unbiased—no one ought to make claims like that—however not less than we managed how our digital folks offered themselves.

Our experiments are completed; it’s time to point out you what we created. We’ve taken 5 books, generated quick podcasts summarizing every with each NotebookLM and our toolchain, and posted each units on oreilly.com and in our studying platform. We’ll be including extra books in 2025. Hearken to them—see what works for you. And please tell us what you assume!