Relevant Content Selection through Positional Language ModelsAn Exploratory Analysis

  1. Marta Vicente
  2. Elena Lloret
Aldizkaria:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Argitalpen urtea: 2020

Zenbakia: 65

Orrialdeak: 75-82

Mota: Artikulua

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Beste argitalpen batzuk: Procesamiento del lenguaje natural

Laburpena

Extractive Summarisation, like other areas in Natural Language Processing, has succumbed to the general trend marked by the success of neural approaches. However, the required resources-computational, temporal, data-are not always available. We present an experimental study of a method based on statistical techniques that, exploiting the semantic information from the source and its structure, provides competitive results against the state of the art. We propose a Discourse-Informed approach for Cost-effective Extractive Summarisation (DICES). DICES is an unsupervised, lightweight and adaptable framework that requires neither training data nor high-performance computing resources to achieve promising results.

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Finantzaketari buruzko informazioa

This research results from work partially funded by Generalitat Valenciana (SIIA PROMETEU/2018/089) and the Spanish Government—ModeLang (RTI2018-094653-B-C22) and INTEGER (RTI2018-094649-BI00). It is also based upon work from COST Action Multi3Generation (CA18231).

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