SUMMAC: a text summarization evaluation

TLDR

SUMMAC has developed new extrinsic and intrinsic evaluation methods for summaries, and its approach is relevant to other NLP tasks with many acceptable outputs lacking automatic comparison. The study examines tradeoffs and challenges of SUMMAC evaluation, outlining lessons learned, impacts, and future directions. Accurate systems for topic‑related summaries relied on term frequency, co‑occurrence statistics, and vocabulary overlap between passages. Automatic summarization proves highly effective for relevance assessment on news, with 17 %‑length summaries doubling decision speed without accuracy loss, high intelligibility, though statistical methods offer no advantage for generic summaries.

Abstract

The TIPSTER Text Summarization Evaluation (SUMMAC) has developed several new extrinsic and intrinsic methods for evaluating summaries. It has established definitively that automatic text summarization is very effective in relevance assessment tasks on news articles. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in accuracy. Analysis of feedback forms filled in after each decision indicated that the intelligibility of present-day machine-generated summaries is high. Systems that performed most accurately in the production of indicative and informative topic-related summaries used term frequency and co-occurrence statistics, and vocabulary overlap comparisons between text passages. However, in the absence of a topic, these statistical methods do not appear to provide any additional leverage: in the case of generic summaries, the systems were indistinguishable in accuracy. The paper discusses some of the tradeoffs and challenges faced by the evaluation, and also lists some of the lessons learned, impacts, and possible future directions. The evaluation methods used in the SUMMAC evaluation are of interest to both summarization evaluation as well as evaluation of other ‘output-related’ NLP technologies, where there may be many potentially acceptable outputs, with no automatic way to compare them.