Text-Independent Extractive Speech Summarization for Low-Resource Languages

Automatic speech summarization presents significant challenges when spoken-language data are limited and conventional text-based resources are unavailable or insufficient. Text-Independent Extractive Speech Summarization for Low-Resource Languages provides a focused technical examination of speech summarization, extractive approaches, low-resource language processing, speech analysis, natural language processing, and machine learning. The book brings together concepts from speech technology, computational linguistics, artificial intelligence, information retrieval, and language-resource development.

The book introduces the foundations of automatic speech summarization and examines the challenges involved in converting lengthy spoken content into concise representations of its most informative material. Spoken communication differs from written text because speech can contain repetitions, disfluencies, pauses, incomplete expressions, variations in speaking style, and other characteristics that influence computational analysis. These properties make speech summarization a distinct problem requiring appropriate processing and representation methods.

A central focus is placed on text-independent extractive summarization. Extractive methods identify and select informative portions of an original speech signal or associated speech segments rather than generating completely new textual content. A text-independent approach places greater emphasis on information that can be derived directly from speech characteristics, reducing dependence on extensive language-specific textual resources. This perspective is particularly relevant when working with languages for which large annotated text corpora, linguistic resources, or sophisticated language-processing tools may be limited.