Text Summarizer

What this tool is used for

• Getting the gist of a long article before deciding whether to read it.
• Producing a first-draft summary you then rewrite in your own words.
• Identifying which sentences a document leans on most heavily.
• Shortening a report for a covering note.
• Checking whether a piece has a clear thesis by seeing what surfaces.

Frequently Asked Questions

How does this summarise?

Extractively — it scores existing sentences by term frequency and position and keeps the highest-ranked, so every sentence in the output appears verbatim in the source. It does not paraphrase or generate new text.

What is the difference from abstractive summarising?

Abstractive systems (language models) write new sentences and can genuinely condense an idea. Extractive methods cannot, but they also cannot hallucinate — nothing appears in the summary that was not in the original.

Why does it need several sentences to work?

Because ranking requires something to rank. Below about three sentences there is no meaningful selection, so it declines rather than returning the input unchanged and calling it a summary.

Does it work on non-English text?

Sentence splitting handles CJK terminators (。！？) as well as Latin ones, so it segments correctly. The term-frequency scoring is language-agnostic, though stopword removal is tuned for English.

Why does the summary read disjointedly?

Because extracted sentences lose their connective tissue — a sentence starting "However," or "This means" is pulled from context. That is the inherent limitation of extraction, not a defect in the ranking.

How are sentences scored?

By the words they contain — sentences carrying more of the document's frequent, distinctive terms rank higher. It is a frequency heuristic, which is why a sentence packed with keywords can outrank the one that actually states the conclusion.

What length should a summary be?

Enough sentences to keep the argument's shape, usually 10-20% of the original. Below that an extractive summary reads as disconnected quotes, because it cannot bridge the gaps the way a written summary would.

Common errors and gotchas

• Publishing the output as prose, since extractive summaries read as disconnected sentences.
• Expecting it to understand the text, when it is scoring sentences statistically.
• Summarising a document whose meaning lives in structure — a table, a list, a sequence.
• Assuming the most frequent terms are the most important ones.
• Losing a negation or a qualifier that reverses a sentence's meaning when it is lifted out of context.