8,579 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
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Abstracts and Abstracting in Knowledge Discovery
published or submitted for publicatio
Automatic summarising: factors and directions
This position paper suggests that progress with automatic summarising demands
a better research methodology and a carefully focussed research strategy. In
order to develop effective procedures it is necessary to identify and respond
to the context factors, i.e. input, purpose, and output factors, that bear on
summarising and its evaluation. The paper analyses and illustrates these
factors and their implications for evaluation. It then argues that this
analysis, together with the state of the art and the intrinsic difficulty of
summarising, imply a nearer-term strategy concentrating on shallow, but not
surface, text analysis and on indicative summarising. This is illustrated with
current work, from which a potentially productive research programme can be
developed
Jumping Finite Automata for Tweet Comprehension
Every day, over one billion social media text messages are generated worldwide, which provides abundant information that can lead to improvements in lives of people through evidence-based decision making. Twitter is rich in such data but there are a number of technical challenges in comprehending tweets including ambiguity of the language used in tweets which is exacerbated in under resourced languages. This paper presents an approach based on Jumping Finite Automata for automatic comprehension of tweets. We construct a WordNet for the language of Kenya (WoLK) based on analysis of tweet structure, formalize the space of tweet variation and abstract the space on a Finite Automata. In addition, we present a software tool called Automata-Aided Tweet Comprehension (ATC) tool that takes raw tweets as input, preprocesses, recognise the syntax and extracts semantic information to 86% success rate
Morphological Analysis as Classification: an Inductive-Learning Approach
Morphological analysis is an important subtask in text-to-speech conversion,
hyphenation, and other language engineering tasks. The traditional approach to
performing morphological analysis is to combine a morpheme lexicon, sets of
(linguistic) rules, and heuristics to find a most probable analysis. In
contrast we present an inductive learning approach in which morphological
analysis is reformulated as a segmentation task. We report on a number of
experiments in which five inductive learning algorithms are applied to three
variations of the task of morphological analysis. Results show (i) that the
generalisation performance of the algorithms is good, and (ii) that the lazy
learning algorithm IB1-IG performs best on all three tasks. We conclude that
lazy learning of morphological analysis as a classification task is indeed a
viable approach; moreover, it has the strong advantages over the traditional
approach of avoiding the knowledge-acquisition bottleneck, being fast and
deterministic in learning and processing, and being language-independent.Comment: 11 pages, 5 encapsulated postscript figures, uses non-standard NeMLaP
proceedings style nemlap.sty; inputs ipamacs (international phonetic
alphabet) and epsf macro
Template Mining for Information Extraction from Digital Documents
published or submitted for publicatio
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