Relevant Content Selection through Positional Language Models: An Exploratory Analysis

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 stat...

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Bibliographic Details
Published in:Procesamiento del Lenguaje Natural 2020-09, Vol.65, p.75
Main Authors: Vicente, Marta, Lloret, Elena Elena
Format: Article
Language:English
Subjects:
Access to information
Automatic summarization
Language
Language modeling
Natural language processing
Semantics
Online Access:Get full text
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Summary: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.
ISSN:1135-5948
1989-7553
DOI:10.26342/2020-65-9