637 research outputs found
Towards End-to-end Speech-to-text Summarization
Speech-to-text (S2T) summarization is a time-saving technique for filtering
and keeping up with the broadcast news uploaded online on a daily basis. The
rise of large language models from deep learning with impressive text
generation capabilities has placed the research focus on summarization systems
that produce paraphrased compact versions of the document content, also known
as abstractive summaries. End-to-end (E2E) modelling of S2T abstractive
summarization is a promising approach that offers the possibility of generating
rich latent representations that leverage non-verbal and acoustic information,
as opposed to the use of only linguistic information from automatically
generated transcripts in cascade systems. However, the few literature on E2E
modelling of this task fails on exploring different domains, namely broadcast
news, which is challenging domain where large and diversified volumes of data
are presented to the user every day. We model S2T summarization both with a
cascade and an E2E system for a corpus of broadcast news in French. Our novel
E2E model leverages external data by resorting to transfer learning from a
pre-trained T2T summarizer. Experiments show that both our cascade and E2E
abstractive summarizers are stronger than an extractive baseline. However, the
performance of the E2E model still lies behind the cascade one, which is object
of an extensive analysis that includes future directions to close that gap.Comment: Accepted to the 26th International Conference of Text, Speech and
Dialogue (TSD2023
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
Abstractive community detection is an important spoken language understanding
task, whose goal is to group utterances in a conversation according to whether
they can be jointly summarized by a common abstractive sentence. This paper
provides a novel approach to this task. We first introduce a neural contextual
utterance encoder featuring three types of self-attention mechanisms. We then
train it using the siamese and triplet energy-based meta-architectures.
Experiments on the AMI corpus show that our system outperforms multiple
energy-based and non-energy based baselines from the state-of-the-art. Code and
data are publicly available.Comment: Update baseline
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