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Sumarização de texto

Comprimir um texto longo em uma versão mais curta e fiel

Linguagem e conhecimentoIniciante #04
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O texto completo é apresentado em inglês; o título e o resumo estão traduzidos.

O QUE ESTA CAPACIDADE SIGNIFICA

Takes a long document and returns a shorter version that keeps the key information. It splits into extractive (selecting sentences) and abstractive (rewriting in new words) styles, the latter now dominant. Unlike free generation it has an explicit compression target, and unlike question answering it is not aimed at one query but should cover the whole thread.

Como é feita tecnicamente

The classic approach is a sequence-to-sequence attention model producing abstractive summaries, with rewriting ability coming from large-scale pre-training. Long documents are usually handled hierarchically or by a map-reduce scheme that summarises chunks and then combines them. Controlled summarisation passes length, angle or audience constraints through the prompt or light fine-tuning to steer style.

Produtos representativos

8

Organizações relacionadas

Usos típicos

  • Quick reads of news and reports
  • Meeting and call minutes
  • Literature triage and paper skims
  • Rolling summaries of tickets and email

Como avaliar se funciona bem

ROUGE
N-gram overlap with reference summaries
BERTScore
Semantic-embedding similarity, more tolerant than literal overlap
Factual consistency
Share of statements conflicting with the source, as in FactCC-style evaluation

Limites e dificuldades

  • Middle sections of long documents are often dropped; models favour the start and end
  • Abstractive models splice facts from different sentences into claims the source never made
  • On ambiguous or multi-sided texts, minority views are often reported as the majority

Conceitos por trás