# Enumeration of Extractive Oracle Summaries

## TL;DR

This paper introduces an ILP-based method to enumerate all extractive oracle summaries, revealing potential for improving summarization performance and better aligning automatic metrics with human judgment.

## Contribution

It presents a novel ILP formulation and enumeration algorithm for extractive oracle summaries, enhancing analysis of summarization quality and evaluation.

## Key findings

- Enumerated oracle summaries correlate better with human judgment.
- Room for improvement in extractive summarization performance.
- F-measures from enumeration outperform single oracle summaries.

## Abstract

To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also propose an algorithm that enumerates all of the oracle summaries for a set of reference summaries to exploit F-measures that evaluate which system summaries contain how many sentences that are extracted as an oracle summary. Our experimental results obtained from Document Understanding Conference (DUC) corpora demonstrated the following: (1) room still exists to improve the performance of extractive summarization; (2) the F-measures derived from the enumerated oracle summaries have significantly stronger correlations with human judgment than those derived from single oracle summaries.