New approach to summarize books

In the realm of natural language processing summarization algorithms play a crucial role in condensing large volumes of text into more manageable, informative summaries. Traditionally, these algorithms have operated on a chunk-by-chunk basis, processing sequential blocks of text without a deep understanding of the overall thematic structure. However, this approach often overlooks the semantic relationships between different parts of the text, leading to summaries that might miss the bigger picture or the nuanced interplay of themes.

To address these limitations, I embarked on developing a “semantic summarization” technique, leveraging advancements in machine learning and NLP. Unlike traditional methods, this approach doesn’t process text linearly. Instead, it begins by dividing the text into segments, not just based on their order but by analyzing their semantic content. The goal is to group text segments into clusters that represent the main themes or topics of the document, thereby preserving the richness and depth of the original content.

The Process

  1. Text Segmentation: The first step involves breaking down the text into manageable segments. Rather than doing this sequentially, segments are created based on thematic similarities, ensuring that each piece of text is evaluated for its content and meaning.

  2. Vectorization: Each segment is then transformed into a vector representation using models like OpenAI’s Ada for deep semantic understanding. This process enables us to capture the essence of each text segment in a form that machines can process.

  3. Clustering: With vectors in hand, we apply a clustering algorithm that groups segments into clusters based on semantic similarity. This step is crucial as it determines the main themes or chapters of the text without predefined boundaries.

  4. Title Generation and Text Association: Each cluster is given a title that reflects its overarching theme, and the texts within each cluster are concatenated. This approach not only simplifies the text but also ensures that the summary retains the original’s thematic structure.

  5. Summarization: Finally, using OpenAI’s powerful models, we summarize the concatenated texts of each cluster. This step distills the essence of each thematic cluster into a concise summary.

Semantic vs. Traditional Summarization

The key difference between semantic summarization and traditional methods lies in the former’s ability to understand and preserve the thematic essence of the text. By clustering text semantically, the summary not only remains faithful to the original content but also presents it in a structure that highlights the primary themes, offering readers a coherent and comprehensive overview.

A Hybrid Approach?

Considering the strengths of both semantic and traditional summarization methods, a hybrid approach might offer the best of both worlds. Such an approach could maintain the chronological order of chapters while enriching the summarization process with deep thematic insights. This could be particularly effective for large books, where preserving the narrative flow is as important as capturing the semantic depth.

Have you experimented with semantic summarization or similar algorithms for processing large volumes of text, such as books? Do you think a hybrid approach that combines semantic richness with the orderly progression of traditional methods could offer a more effective summarization solution? I’m eager to hear about your experiences and thoughts on leveraging these advanced NLP techniques for better text comprehension and summarization.


Hi @Mikiane - This is really interesting. During the second half of last year I’ve piloted some work around large document summarization with the similar aim of preserving the overall structure and logic of the document. For some other solutions I have been building, I have also experimented with thematic text segmentation for the purpose of clustering similar paragraphs together and subsequently processing these further for analysis.

Your approach sounds really interesting.

If you are open to it, I would be really interested in connecting and exchange some experiences and ideas on this further.

New approach to identifying AI fluff language:

AI wants to rewrite nothing.

To get purposeful text, instead of the meandering fluff of the latest models, let’s have gpt-4-0314 process this according to my instructions and make it presentable.

Semantic summarization is a technique that involves text segmentation, vectorization, clustering, title generation, and summarization. It differs from traditional methods by focusing on understanding and preserving the thematic essence of the text. A hybrid approach combining semantic and traditional summarization could be beneficial for large books, maintaining chronological order while capturing semantic depth.

To implement the semantic summarization process, begin by segmenting the text into thematically similar sections instead of sequential chunks. Next, convert each segment into a vector representation using models like OpenAI's text-embeddings-3-large for deep semantic understanding. Then, apply a clustering algorithm to group the vectors based on semantic similarity, identifying the main themes or topics. Assign a title to each cluster that reflects its overarching theme and concatenate the texts within each cluster. Finally, use powerful models like OpenAI’s to generate a concise summary for each thematic cluster, resulting in a coherent and comprehensive overview of the original text.

You can also use AI to find out if it is unfeasable and undeveloped:

The description provided does not offer specific details on how to segment the text into thematically similar sections. To perform this step, one would need more information on the algorithms or techniques used for identifying and separating thematic segments. This could involve natural language processing methods, keyword extraction, or topic modeling techniques like Latent Dirichlet Allocation (LDA). Further clarification and guidance on the preferred method for text segmentation would be necessary to implement this step effectively.


Absolutely interested !
Let’s connect via email

Why is it necessary to preserve the overall structure? What does this enable you to do that you wouldn’t by simply chunking, embedding and vectorizing? Genuinely curious.

To begin with, it’s a use case specific consideration. The documents I am applying summarization on may be different from yours.

Keeping this in mind: If there’s a 100+ page long document with 10 or so specific sections, then having a summary that preserves this structure will make it easier for the reader to later go back to the original document and read up on details if that is desired. Often the summary is just the starting point to get a better sense of what is important in the document and not a substitute for the full document.

I also consider it more intuitive to break up documents by section as opposed to fix chunks that don’t take the document’s logic and structure into account as it can affect the quality of the summary.

Aha! Semantic Chunking!

Absolutely. Not only case by case, but sometimes document by document. I have multiple custom embedding schemes within one dataset because different documents sometimes require different embedding methods: sometimes I add a summary of the entire document to each chunk, sometimes I summarize each individual chunk, sometimes I just place a overall description in each chunk, and sometimes I don’t add anything because the title and other metadata suffices.


This is interesting. Are you adding this as meta-data when adding the embedding to a vector database?

Thanks for sharing. I’m summarizing long documents (conversations) that don’t have natural breaks but do have clear semantically different sections. One issue i’m having is having the summary follow the order of the appearance in the conversation

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Yes. “summary” is an an object property which is embedded along with “content”. The idea is that it can be used, when needed, to increase the SNR (signal-to-noise ration) on cosine similarity searches.

But I also use it to create “context similarity” between documents. For example, I chunk a school board meeting agenda where part is in chunk 1 and part in chunk 2. I add summary of the meeting document to both chunks. I found that this increased the possibility that any general question about that particular meeting would include both chunks.