Sentence Embeddings as an intermediate target in end-to-end summarisation

Maciej Zembrzuski; Saad Mahamood

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arXiv · 2505.03481

Sentence Embeddings as an intermediate target in end-to-end summarisation

Maciej Zembrzuski·Saad Mahamood

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Abstract

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.

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Maciej Zembrzuski, Saad Mahamood. 2025-05-06. Sentence Embeddings as an intermediate target in end-to-end summarisation. https://arxiv.org/abs/2505.03481

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