Automatic Summarization of Natural Language

TL;DR

This paper reviews the progress and challenges in automatic natural language summarization, contrasting extractive and abstractive methods, and discusses future directions for improved measurement and understanding.

Contribution

It provides a comprehensive comparison of summarization techniques, evaluates current metrics, and offers insights for advancing abstractive summarization research.

Findings

Extractive methods are more developed than abstractive ones.

Current metrics like Rouge and BLEU have limitations in measuring quality.

Insights suggest new directions for understanding and evaluating summarization.

Abstract

Automatic summarization of natural language is a current topic in computer science research and industry, studied for decades because of its usefulness across multiple domains. For example, summarization is necessary to create reviews such as this one. Research and applications have achieved some success in extractive summarization (where key sentences are curated), however, abstractive summarization (synthesis and re-stating) is a hard problem and generally unsolved in computer science. This literature review contrasts historical progress up through current state of the art, comparing dimensions such as: extractive vs. abstractive, supervised vs. unsupervised, NLP (Natural Language Processing) vs Knowledge-based, deep learning vs algorithms, structured vs. unstructured sources, and measurement metrics such as Rouge and BLEU. Multiple dimensions are contrasted since current research uses combinations of approaches as seen in the review matrix. Throughout this summary, synthesis and critique is provided. This review concludes with insights for improved abstractive summarization measurement, with surprising implications for detecting understanding and comprehension in general.