35,031 research outputs found
Generating Abstractive Summaries from Meeting Transcripts
Summaries of meetings are very important as they convey the essential content
of discussions in a concise form. Generally, it is time consuming to read and
understand the whole documents. Therefore, summaries play an important role as
the readers are interested in only the important context of discussions. In
this work, we address the task of meeting document summarization. Automatic
summarization systems on meeting conversations developed so far have been
primarily extractive, resulting in unacceptable summaries that are hard to
read. The extracted utterances contain disfluencies that affect the quality of
the extractive summaries. To make summaries much more readable, we propose an
approach to generating abstractive summaries by fusing important content from
several utterances. We first separate meeting transcripts into various topic
segments, and then identify the important utterances in each segment using a
supervised learning approach. The important utterances are then combined
together to generate a one-sentence summary. In the text generation step, the
dependency parses of the utterances in each segment are combined together to
create a directed graph. The most informative and well-formed sub-graph
obtained by integer linear programming (ILP) is selected to generate a
one-sentence summary for each topic segment. The ILP formulation reduces
disfluencies by leveraging grammatical relations that are more prominent in
non-conversational style of text, and therefore generates summaries that is
comparable to human-written abstractive summaries. Experimental results show
that our method can generate more informative summaries than the baselines. In
addition, readability assessments by human judges as well as log-likelihood
estimates obtained from the dependency parser show that our generated summaries
are significantly readable and well-formed.Comment: 10 pages, Proceedings of the 2015 ACM Symposium on Document
Engineering, DocEng' 201
Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus
The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning
Enhanced amplitude modulations contribute to the Lombard intelligibility benefit: Evidence from the Nijmegen Corpus of Lombard Speech
Speakers adjust their voice when talking in noise, which is known as Lombard speech. These acoustic adjustments facilitate speech comprehension in noise relative to plain speech (i.e., speech produced in quiet). However, exactly which characteristics of Lombard speech drive this intelligibility benefit in noise remains unclear. This study assessed the contribution of enhanced amplitude modulations to the Lombard speech intelligibility benefit by demonstrating that (1) native speakers of Dutch in the Nijmegen Corpus of Lombard Speech (NiCLS) produce more pronounced amplitude modulations in noise vs. in quiet; (2) more enhanced amplitude modulations correlate positively with intelligibility in a speech-in-noise perception experiment; (3) transplanting the amplitude modulations from Lombard speech onto plain speech leads to an intelligibility improvement, suggesting that enhanced amplitude modulations in Lombard speech contribute towards intelligibility in noise. Results are discussed in light of recent neurobiological models of speech perception with reference to neural oscillators phase-locking to the amplitude modulations in speech, guiding the processing of speech
Effects of two teaching methods of connected speech in a Polish EFL classroom
The results demonstrate that in general, NF proved more effective than NNF. With regard to individual processes of connected speech, NF was more effective in production, whereas no such effect was found for perception
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
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