  In this paper, we report on indexing performance by a state-of-the-art
keyphrase indexer, Maui, when paired with a text extraction procedure called
text denoising. Text denoising is a method that extracts the denoised text,
comprising the content-rich sentences, from full texts. The performance of the
keyphrase indexer is demonstrated on three standard corpora collected from
three domains, namely food and agriculture, high energy physics, and biomedical
science. Maui is trained using the full texts and denoised texts. The indexer,
using its trained models, then extracts keyphrases from test sets comprising
full texts, and their denoised and noise parts (i.e., the part of texts that
remains after denoising). Experimental findings show that against a gold
standard, the denoised-text-trained indexer indexing full texts, performs
either better than or as good as its benchmark performance produced by a
full-text-trained indexer indexing full texts.
