Oversummarization

Oversummarization is the outcome of creating a summary that omits essential information, oversimplifies, or distorts the original text due to excessive compression or misalignment between the source and the summarization criteria. It can occur in both human and automated summaries and is defined by loss of crucial details or misrepresentation of meaning rather than mere brevity.

In computational settings, oversummarization is a risk for extractive and abstractive summarization systems. A summary may be too short or overly generalized, failing to preserve methods, results, or important nuances. This problem can also arise when editors, journalists, or educators compress material beyond what is needed, sacrificing accuracy or context.

Causes include aggressive length constraints, mismatched objectives between what the summary should convey and what is retained, and biases in training data or evaluation metrics. In AI models, tendencies toward hallucination or over-generalization can contribute to oversummarization, as can optimization pressure to maximize conciseness over fidelity.

Characteristics of oversummarization include loss of key points, distortion of meaning, and factual inaccuracies in the condensed text. The impact is reduced informativeness, potential misinterpretation, and diminished trust in the summary and its source.

Detection and measurement rely on human judgment and, in AI contexts, fidelity and saliency assessments. Automatic metrics may fail to capture important omissions. Mitigation strategies include calibrating length and salience targets, applying content-selection constraints, enabling fact-checking and truthfulness checks, and combining automatic summarization with human review or post-editing. Providing user control over emphasis can also help preserve essential information.