# Comparative Document Summarisation via Classification

## TL;DR

This paper introduces a novel, scalable approach to comparative document summarisation by framing it as a classification problem, leveraging gradient-based optimisation to produce summaries that distinguish document groups effectively.

## Contribution

It formulates comparative summarisation as a classification task using maximum mean discrepancy, enabling scalable evaluation and improved summarisation quality over baseline methods.

## Key findings

- Gradient-based optimisation outperforms baseline approaches in automatic evaluations.
- Summaries from gradient optimisation elicit 7% more accurate human classification.
- The approach is effective across diverse use cases like comparing sources or viewpoints.

## Abstract

This paper considers extractive summarisation in a comparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13 months. We observe that gradient-based optimisation outperforms discrete and baseline approaches in 14 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.