54,253 research outputs found
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Summarization of Films and Documentaries Based on Subtitles and Scripts
We assess the performance of generic text summarization algorithms applied to
films and documentaries, using the well-known behavior of summarization of news
articles as reference. We use three datasets: (i) news articles, (ii) film
scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics
are used for comparing generated summaries against news abstracts, plot
summaries, and synopses. We show that the best performing algorithms are LSA,
for news articles and documentaries, and LexRank and Support Sets, for films.
Despite the different nature of films and documentaries, their relative
behavior is in accordance with that obtained for news articles.Comment: 7 pages, 9 tables, 4 figures, submitted to Pattern Recognition
Letters (Elsevier
A tool set for the quick and efficient exploration of large document collections
We are presenting a set of multilingual text analysis tools that can help
analysts in any field to explore large document collections quickly in order to
determine whether the documents contain information of interest, and to find
the relevant text passages. The automatic tool, which currently exists as a
fully functional prototype, is expected to be particularly useful when users
repeatedly have to sieve through large collections of documents such as those
downloaded automatically from the internet. The proposed system takes a whole
document collection as input. It first carries out some automatic analysis
tasks (named entity recognition, geo-coding, clustering, term extraction),
annotates the texts with the generated meta-information and stores the
meta-information in a database. The system then generates a zoomable and
hyperlinked geographic map enhanced with information on entities and terms
found. When the system is used on a regular basis, it builds up a historical
database that contains information on which names have been mentioned together
with which other names or places, and users can query this database to retrieve
information extracted in the past.Comment: 10 page
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