Relevant Content Selection through Positional Language Models: An Exploratory Analysis

Resumen

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.