720 research outputs found
A framework for the Comparative analysis of text summarization techniques
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceWe see that with the boom of information technology and IOT (Internet of things), the size of information which is basically data is increasing at an alarming rate. This information can always be harnessed and if channeled into the right direction, we can always find meaningful information. But the problem is this data is not always numerical and there would be problems where the data would be completely textual, and some meaning has to be derived from it. If one would have to go through these texts manually, it would take hours or even days to get a concise and meaningful information out of the text. This is where a need for an automatic summarizer arises easing manual intervention, reducing time and cost but at the same time retaining the key information held by these texts. In the recent years, new methods and approaches have been developed which would help us to do so. These approaches are implemented in lot of domains, for example, Search engines provide snippets as document previews, while news websites produce shortened descriptions of news subjects, usually as headlines, to make surfing easier.
Broadly speaking, there are mainly two ways of text summarization – extractive and abstractive summarization. Extractive summarization is the approach in which important sections of the whole text are filtered out to form the condensed form of the text. While the abstractive summarization is the approach in which the text as a whole is interpreted and examined and after discerning the meaning of the text, sentences are generated by the model itself describing the important points in a concise way
Text pre-processing tool to increase the exactness of experimental results in summarization solutions
For years, and nowadays even more because of the ease of access to information, countless scientific documents that cover all branches of human knowledge are generated. These documents, consisting mostly of text, are stored in digital libraries that are increasingly consenting access and manipulation. This has allowed these repositories of documents to be used for research work of great interest, particularly those related to evaluation of automatic summaries through experimentation. In this area of computer science, the experimental results of many of the published works are obtained using document collections, some known and others not so much, but without specifying all the special considerations to achieve said results. This produces an unfair competition in the realization of experiments when comparing results and does not allow to be objective in the obtained conclusions.
This paper presents a text document manipulation tool to increase the exactness of results when obtaining, evaluating and comparing automatic summaries from different corpora. This work has been motivated by the need to have a tool that allows to process documents, split their content properly and make sure that each text snippet does not lose its contextual information. Applying the model proposed to a set of free-access scientific papers has been successful.XV Workshop Bases de Datos y MinerÃa de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI
Text pre-processing tool to increase the exactness of experimental results in summarization solutions
For years, and nowadays even more because of the ease of access to information, countless scientific documents that cover all branches of human knowledge are generated. These documents, consisting mostly of text, are stored in digital libraries that are increasingly consenting access and manipulation. This has allowed these repositories of documents to be used for research work of great interest, particularly those related to evaluation of automatic summaries through experimentation. In this area of computer science, the experimental results of many of the published works are obtained using document collections, some known and others not so much, but without specifying all the special considerations to achieve said results. This produces an unfair competition in the realization of experiments when comparing results and does not allow to be objective in the obtained conclusions.
This paper presents a text document manipulation tool to increase the exactness of results when obtaining, evaluating and comparing automatic summaries from different corpora. This work has been motivated by the need to have a tool that allows to process documents, split their content properly and make sure that each text snippet does not lose its contextual information. Applying the model proposed to a set of free-access scientific papers has been successful.XV Workshop Bases de Datos y MinerÃa de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI
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Sentence Fusion for Multidocument News Summarization
The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies
Towards Personalized and Human-in-the-Loop Document Summarization
The ubiquitous availability of computing devices and the widespread use of
the internet have generated a large amount of data continuously. Therefore, the
amount of available information on any given topic is far beyond humans'
processing capacity to properly process, causing what is known as information
overload. To efficiently cope with large amounts of information and generate
content with significant value to users, we require identifying, merging and
summarising information. Data summaries can help gather related information and
collect it into a shorter format that enables answering complicated questions,
gaining new insight and discovering conceptual boundaries.
This thesis focuses on three main challenges to alleviate information
overload using novel summarisation techniques. It further intends to facilitate
the analysis of documents to support personalised information extraction. This
thesis separates the research issues into four areas, covering (i) feature
engineering in document summarisation, (ii) traditional static and inflexible
summaries, (iii) traditional generic summarisation approaches, and (iv) the
need for reference summaries. We propose novel approaches to tackle these
challenges, by: i)enabling automatic intelligent feature engineering, ii)
enabling flexible and interactive summarisation, iii) utilising intelligent and
personalised summarisation approaches. The experimental results prove the
efficiency of the proposed approaches compared to other state-of-the-art
models. We further propose solutions to the information overload problem in
different domains through summarisation, covering network traffic data, health
data and business process data.Comment: PhD thesi
Information fusion for automated question answering
Until recently, research efforts in automated Question Answering (QA) have mainly
focused on getting a good understanding of questions to retrieve correct answers. This
includes deep parsing, lookups in ontologies, question typing and machine learning
of answer patterns appropriate to question forms. In contrast, I have focused on the
analysis of the relationships between answer candidates as provided in open domain
QA on multiple documents. I argue that such candidates have intrinsic properties,
partly regardless of the question, and those properties can be exploited to provide better
quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from
different sources. In QA over free text, it is motivated by the frequency with which
different answer candidates are found in different locations, leading to a multiplicity
of answers. The reason for such multiplicity is, in part, the massive amount of data
used for answering, and also its unstructured and heterogeneous content: Besides am¬
biguities in user questions leading to heterogeneity in extractions, systems have to deal
with redundancy, granularity and possible contradictory information. Hence the need
for answer candidate comparison. While frequency has proved to be a significant char¬
acteristic of a correct answer, I evaluate the value of other relationships characterizing
answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬
define the concept of "answer" within an engineering approach to QA based on the
Model-View-Controller (MVC) pattern of user interface design. An "answer model"
is a directed graph in which nodes correspond to entities projected from extractions
and edges convey relationships between such nodes. The graph represents the fusion
of information contained in the set of extractions. Different views of the answer model
can be produced, capturing the fact that the same answer can be expressed and pre¬
sented in various ways: picture, video, sound, written or spoken language, or a formal
data structure. Within this framework, an answer is a structured object contained in the
model and retrieved by a strategy to build a particular view depending on the end user
(or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence,
inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬
proves answer extraction accuracy. It also proves to be more robust to incorrect answer
candidates than traditional techniques. Qualitatively, models provide meta-information
encoded by relationships that allow shallow reasoning to help organize and generate
the final output
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