Current neural network-based methods to the problem of document summarisation struggle when applied to datasets containing large inputs. In this paper we propose a new approach to the challenge of content-selection when dealing with end-to-end summarisation of user reviews of accommodations. We show that by combining an extractive approach with externally pre-trained sentence level embeddings in an addition to an abstractive summarisation model we can outperform existing methods when this is applied to the task of summarising a large input dataset. We also prove that predicting sentence level embedding of a summary increases the quality of an end-to-end system for loosely aligned source to target corpora, than compared to commonly predicting probability distributions of sentence selection.
View on arXiv@article{zembrzuski2025_2505.03481,
title={ Sentence Embeddings as an intermediate target in end-to-end summarisation },
author={ Maciej Zembrzuski and Saad Mahamood },
journal={arXiv preprint arXiv:2505.03481},
year={ 2025 }
}