How To Summarize Long Text
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Extractive versus abstractive
TextRank (Mihalcea and Tarau, 2004) applies the PageRank algorithm to a graph of sentences. Each sentence is a node, and edges are weighted by a similarity metric (typically cosine similarity of term vectors or simple word overlap normalized by length). Running PageRank over that graph ranks sentences by how “central” they are across the document, and the top-N highest-ranked sentences form the summary. TextRank is unsupervised, fast, and works without training data, which is why it was the dominant open-source summarization technique for roughly fifteen years. Its weakness: it cannot rewrite awkward sentences or combine ideas spread across the source.
TextRank and graph-based methods
Older summarizers rank sentences by the frequency of their terms: sentences containing the most common content words (minus stop words) are considered important. TF-IDF refines this by weighting rare terms higher, under the theory that a term unique to this document is more indicative of its topic than a term common across all documents. These approaches work passably for news articles but struggle with anything that uses a specialized vocabulary evenly throughout.
Frequency-based and TF-IDF approaches
A modern LLM prompted with “Summarize the following text in three sentences” produces far better output than TextRank in most subjective evaluations. The model can rewrite, combine ideas, match a requested tone, and produce output that reads as though written fresh. The trade-offs: computational cost, possible hallucination, and lack of transparent attribution. For high-stakes summaries (legal, medical, financial), pair LLM output with an extractive pass that surfaces the source sentences the claims are based on.
LLM-based abstractive summarization
Average reading speed is 200 to 250 words per minute for general text, faster for recreational reading and slower for dense technical material. A reader will typically allocate 10 to 20 percent of the time it would take to read the original. For a 5,000-word article (20 minutes), that means a 200-word summary (1 minute). Aim the word count at the reader’s likely time budget rather than a fixed ratio of the original length.
Length targets by context
A summary that collapses a multi-section document into one paragraph often loses the structural information readers need. For a report with distinct sections, preserve the section boundaries by summarizing each section into one or two sentences and keeping the section headings. This produces a scannable summary with the same top-level structure as the original, which readers can navigate like a table of contents. Abstractive summarizers tend to flatten structure by default; ask them explicitly to preserve it.
Reading time as a target
Read the summary without the source and ask: would someone who has not read the original understand the main points? Then read the source and ask: did the summary miss anything essential? Does it include anything not supported by the source? The first check catches summaries that are too abstract to be useful. The second catches hallucinations and distortions. For extractive methods, verify the sentences are in a sensible order. For abstractive methods, verify every claim traces back to the source.
Preserving structure
Legal text, medical records, technical specifications, and code all have domain conventions that general summarizers miss. Legal text needs every obligation preserved. Medical records need units and dosages intact. Technical specs need numeric values exact. For these domains, general-purpose summarization is a starting point, not a production-ready output. Either use a domain-tuned model or apply a human review pass on anything consequential.