Abridgable

The adjective 'abridgable' describes something that is capable of being shortened or condensed without significantly altering its essential meaning or content. It implies that the subject matter can be reduced in size or scope while still retaining its core information and value. Texts, legal documents, performances, and even datasets can be abridgable, depending on their structure and the intended purpose of the abridgment. This process often involves eliminating redundant details, secondary information, and stylistic flourishes to present the central concepts in a more concise format. Whether a work is deemed abridgable hinges on the degree to which essential ideas can be extracted and presented in a more condensed version.

Abridgable meaning with examples

• The lengthy novel was deemed abridgable, with the publisher confident a condensed version could attract a wider readership without sacrificing the central plot or character development. They removed long passages, unnecessary subplots, and extraneous descriptions to deliver the core narrative in a more accessible format, keeping it to a manageable length.
• Due to time constraints, the legal team decided the extensive report was abridgable, choosing to summarize the most critical evidence and arguments for presentation in court. They meticulously reviewed the document, omitting tangential details while preserving the central points and findings to deliver a cohesive and impactful message to the judge and jury.
• The complex musical composition proved abridgable; its extended duration could be reduced by strategically shortening certain sections without significantly affecting the overall artistic expression. The composer worked carefully to remove repetitive elements and minor variations to deliver a performance under two hours, without diluting its musical essence.
• The comprehensive scientific dataset was considered abridgable, with researchers focusing on key variables and statistical analyses to extract and visualize the most crucial findings. The project prioritized distilling the complex relationships between the data points, discarding secondary correlations and raw figures to create clear and presentable information.