Spoken documents (or associated multimedia content) are in fact better understood and
reorganized in a way that retrieval/browsing can be performed easily. For example, they are …

Abstract The MATBN Mandarin Chinese broadcast news corpus contains a total of 198
hours of broadcast news from the Public Television Service Foundation (Taiwan) with …

Although Long-Short Term Memory (LSTM) networks and deep Transformers are now
extensively used in offline ASR, it is unclear how best offline systems can be adapted to …

Extractive text or speech summarization manages to select a set of salient sentences from
an original document and concatenate them to form a summary, enabling users to better …

Extractive speech summarization, which purports to select an indicative set of sentences
from a spoken document so as to succinctly represent the most important aspects of the …

Statistical language modeling (LM), which aims to capture the regularities in human natural
language and quantify the acceptability of a given word sequence, has long been an …

In this paper, we consider extractive summarization of broadcast news speech and propose
a unified probabilistic generative framework that combines the sentence generative …

Imperfect speech recognition often leads to degraded performance when exploiting
conventional text-based methods for speech summarization. To alleviate this problem, this …

The primary task of extractive summarization is to automatically select a set of representative
sentences from a text or spoken document that can concisely express the most important …

Statistical language modeling (LM) that intends to quantify the acceptability of a given piece
of text has long been an interesting yet challenging research area. In particular, language …