LCFO: Long Context and Long Form Output Dataset and Benchmarking

TL;DR

This paper introduces LCFO, a comprehensive benchmark for evaluating long document summarization and expansion, including diverse domains, multiple summary lengths, QA alignments, and human and AI evaluation metrics.

Contribution

The paper presents LCFO, a novel dataset and benchmarking framework for long document summarization and expansion with multi-domain, multi-length summaries, and QA alignment.

Findings

GPT-4o-mini outperforms other models in summarization and expansion.

Automatic metrics show low correlation with human scores (~0.4).

Moderate correlation (~0.6) on fluency and attribution evaluations.

Abstract

This paper presents the Long Context and Form Output (LCFO) benchmark, a novel evaluation framework for assessing gradual summarization and summary expansion capabilities across diverse domains. LCFO consists of long input documents (5k words average length), each of which comes with three summaries of different lengths (20%, 10%, and 5% of the input text), as well as approximately 15 questions and answers (QA) related to the input content. Notably, LCFO also provides alignments between specific QA pairs and corresponding summaries in 7 domains. The primary motivation behind providing summaries of different lengths is to establish a controllable framework for generating long texts from shorter inputs, i.e. summary expansion. To establish an evaluation metric framework for summarization and summary expansion, we provide human evaluation scores for human-generated outputs, as well as results from various state-of-the-art large language models (LLMs). GPT-4o-mini achieves best human scores among automatic systems in both summarization and summary expansion tasks (~ +10% and +20%, respectively). It even surpasses human output quality in the case of short summaries (~ +7%). Overall automatic metrics achieve low correlations with human evaluation scores (~ 0.4) but moderate correlation on specific evaluation aspects such as fluency and attribution (~ 0.6).