Final answer:
In extractive summarization, paragraphs that are related to most other paragraphs are given more weight due to their relevance or centrality, with algorithms like Text Rank used to identify such paragraphs.
Step-by-step explanation:
When performing extractive summarization, the criterion that gives more weight to paragraphs that are more related to most other paragraphs is typically referred to as relevance or centrality. This approach assumes that paragraphs with high interconnections to other paragraphs within a text are likely to be more representative of the overall content, and thus, are given prominence in the summary. One common algorithm that takes into account this criterion is the Text Rank algorithm, which models the problem as a graph and uses the connectivity of the nodes (paragraphs) to determine their importance.
In the context of writing and editing, ensuring that paragraphs are unified under a single, clear topic and using background and supporting details for completeness can improve their perceived relevance. Moreover, consistent use of transitions, subheads, and applicable visuals establishes coherence, which in extractive summarization could make them more likely to be selected as key components of the summarized text.
Therefore, when assessing paragraphs for summarization, one would give more weight to those that are both content-rich and have a higher degree of connectivity to other paragraphs. The aim is to capture the essence of the text by identifying and extracting the most significant and representative parts.