Mike HeapMike is an experienced Product Manager who focuses on all the “non-development” areas of My AskAI, from finance and customer success to product design, copywriting, testing and more.
Artificial intelligence (AI) has revolutionized various aspects of our lives, including information processing and consumption and content generation. However, despite the impressive advancements in AI capabilities, certain limitations persist.
One area where these limitations become evident is in the summarization of complex documents. We wanted to explore the challenges AI tools face when summarizing documents, the impact of these limitations on the accuracy of the output, and talk about a few potential solutions to overcome these challenges.
Why can summarizing be hard?
Limitations of current AI techniques
When asked to summarize a book or a document, the task may seem simple for a human. However, for AI tools, the challenge is more nuanced. The primary challenge lies in the way these algorithms process data. Many AI tools, such as vector embeddings-based models, do not read an entire document all at once. Instead, they analyze portions of the content separately, making it difficult for them to capture the overall context and maintain coherence in a summary.
Consequences of incomplete summaries
To circumvent this limitation, some AI tools analyze pages or sections of a document, summarizing each part separately before combining those summaries into a single (hopefully cohesive) output. However, this approach can lead to missing important details or potentially losing the context that connects different sections. The resulting summary may not accurately represent the original document, leading to a subpar user experience and potentially misleading information.
Semantic search and summarization
Limitations of semantic search in summarization
In the case of My AskAI we utilize a semantic search to find the three most relevant pieces of information that can be used to answer a given question. While this approach is effective in many scenarios, it falls short when asked to summarize a document. If you enter “summarize this document” as your ‘question’ then instead of summarizing, the tool searches for terms in the text that resembles "summarize this document," rather than actually following the instruction to summarize the document. Consequently, the ‘summary’ generated might be inaccurate, incomplete or even misleading.
Overcoming semantic search limitations with advanced AI models
To address the limitations of semantic search in summarization, more advanced AI models, such as GPT-4, show promise. With the ability to process more information simultaneously, these models can potentially handle larger sections of a document (more chunks being fed into a prompt), leading to more accurate summarization. By leveraging the power of these sophisticated AI systems, we can overcome the challenges posed by current techniques and enhance the accuracy and relevance of document summaries.
Conclusion
AI tools have made significant strides in information processing, but they still have limitations, particularly when it comes to summarizing complex documents. To ensure accurate results, it's essential to be aware of these limitations and utilize alternative methods or more advanced AI systems when necessary. By staying informed and adopting innovative solutions, we can harness the power of AI-driven summarization to provide valuable insights and improve the overall user experience.
