Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline

TL;DR

This paper introduces a supervised, proposition-level alignment task for summarization, creating new datasets and a baseline model that outperforms heuristic methods in alignment accuracy.

Contribution

It formalizes alignment as a supervised classification task at the proposition level and provides new datasets and a baseline model for this task.

Findings

Supervised model improves alignment quality over unsupervised methods.

New datasets enable better training and evaluation of alignment models.

Proposition-level alignment enhances summarization training data quality.

Abstract

Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection. Despite its assessed utility, the alignment step was mostly approached with heuristic unsupervised methods, typically ROUGE-based, and was never independently optimized or evaluated. In this paper, we propose establishing summary-source alignment as an explicit task, while introducing two major novelties: (1) applying it at the more accurate proposition span level, and (2) approaching it as a supervised classification task. To that end, we created a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. In addition, we crowdsourced dev and test datasets, enabling model development and proper evaluation. Utilizing these data, we present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.