A large language model struggles to generate coherent summaries. Which algorithm design paradigm best guides creating a more effective, modular summary approach?

💡 Explanation

Dynamic programming enables an efficient approach to summarization by breaking down a complex text into overlapping subproblems and storing intermediate results. This memoization, via dynamic programming, avoids redundant computations, therefore generates better summaries, rather than greedy or divide and conquer strategies which don't leverage the overlapping structure of the problem.

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