1,826 research outputs found
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
Some Reflections on the Task of Content Determination in the Context of Multi-Document Summarization of Evolving Events
Despite its importance, the task of summarizing evolving events has received
small attention by researchers in the field of multi-document summariztion. In
a previous paper (Afantenos et al. 2007) we have presented a methodology for
the automatic summarization of documents, emitted by multiple sources, which
describe the evolution of an event. At the heart of this methodology lies the
identification of similarities and differences between the various documents,
in two axes: the synchronic and the diachronic. This is achieved by the
introduction of the notion of Synchronic and Diachronic Relations. Those
relations connect the messages that are found in the documents, resulting thus
in a graph which we call grid. Although the creation of the grid completes the
Document Planning phase of a typical NLG architecture, it can be the case that
the number of messages contained in a grid is very large, exceeding thus the
required compression rate. In this paper we provide some initial thoughts on a
probabilistic model which can be applied at the Content Determination stage,
and which tries to alleviate this problem.Comment: 5 pages, 2 figure
The Intelligent Web
Many people are working on the Semantic Web with the main objective being to enhance web searches. Our proposal is a new research strategy based on the existence of a discrete set of semantic relations for the creation and exploitation of semantic networks on the web. To do so, we have defined in a previous paper (Ălamo, MartĂnez, JaĂ©n) the Rhetoric-Semantic Relation (RSR) based on the results of the Rhetoric Structure Theory. We formulate a general set of RSR capable of building discourse and making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. These knowledge nodes can then be elaborated in the same way. This network structure in terms of RSR makes the objective of developing automatic answering systems possible as well as any other type of utilities oriented towards the exploitation of semantic structure, such as the automatic production of web pages or automatic e-learning generation
Better Document-level Sentiment Analysis from RST Discourse Parsing
Discourse structure is the hidden link between surface features and
document-level properties, such as sentiment polarity. We show that the
discourse analyses produced by Rhetorical Structure Theory (RST) parsers can
improve document-level sentiment analysis, via composition of local information
up the discourse tree. First, we show that reweighting discourse units
according to their position in a dependency representation of the rhetorical
structure can yield substantial improvements on lexicon-based sentiment
analysis. Next, we present a recursive neural network over the RST structure,
which offers significant improvements over classification-based methods.Comment: Published at Empirical Methods in Natural Language Processing (EMNLP
2015
Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations
Case Law has a significant impact on the proceedings of legal cases.
Therefore, the information that can be obtained from previous court cases is
valuable to lawyers and other legal officials when performing their duties.
This paper describes a methodology of applying discourse relations between
sentences when processing text documents related to the legal domain. In this
study, we developed a mechanism to classify the relationships that can be
observed among sentences in transcripts of United States court cases. First, we
defined relationship types that can be observed between sentences in court case
transcripts. Then we classified pairs of sentences according to the
relationship type by combining a machine learning model and a rule-based
approach. The results obtained through our system were evaluated using human
judges. To the best of our knowledge, this is the first study where discourse
relationships between sentences have been used to determine relationships among
sentences in legal court case transcripts.Comment: Conference: 2018 International Conference on Advances in ICT for
Emerging Regions (ICTer
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