Exploring the subtopic-based relationship map strategy for multi-document summarization

In this paper we adapt and explore strategies for generating multi-document summaries based on relationship maps, which represent texts as graphs (maps) of interrelated segments and apply different traversing techniques for producing the summaries. In particular, we focus on the Segmented Bushy Path, a sophisticated method which tries to represent in a summary the main subtopics from source texts while keeping its informativeness. In addition, we also investigate some well-known subtopic segmentation and clustering techniques in order to correctly select the most relevant information to compose the final summary. We show that this subtopic-based method outperforms other methods for multi-document summarization and that achieves state of the art results, competing with the most sophisticated deep summarization methods in the area

Objetos de Aprendizagem no Ensino de Inglês

Este artigo apresenta a produção de material didático para o ensino da língua inglesa, baseado no conceito de objetos de aprendizagem (OA). Um OA funcional deve conter um propósito definido e claro de objetivos, além de não ser tão específico a ponto de impor seu uso a uma só circunstância. Os resultados obtidos com a aplicação dos OA demonstram que estes, são recursos com os quais alunos e professores podem conseguirresultados satisfatórios, que incluem a facilitação do processo de ensino-aprendizagem

Lexical resources for the identification of causative relations in portuguese texts

The identification of causal relations from text is a mature problem in Natural Language Processing. There are a number of resources and tools to aid causative relation extraction in English, but there seems to be a limited number of resources for Portuguese. This paper presents a number of resources which are designed to aid the researcher and the practitioner to extract causative relations from Portuguese texts.FAPESP (grant number: 11/20451-1

A qualitative analysis of a corpus of opinion summaries based on aspects

Aspect-based opinion summarization is the task of automatically generating a summary\ud for some aspects of a specific topic from a set of opinions. In most cases, to evaluate the quality of the automatic summaries, it is necessary to have a reference corpus of human\ud summaries to analyze how similar they are. The scarcity of corpora in that task has been a limiting factor for many research works. In this paper, we introduce OpiSums-PT, a corpus of extractive and abstractive summaries of opinions written in Brazilian Portuguese. We use this corpus to analyze how similar human summaries are and how people take into account the issues of aspect coverage and sentimento orientation to generate manual summaries. The results of these analyses show that human summaries are diversified and people generate summaries only for some aspects, keeping the overall sentiment orientation with little variation.Samsung Eletrônica da Amazônia Ltda

Exploration of automatic methods for multi-document summarization using discourse models

A sumarização automática multidocumento visa à produção de um sumário a partir de um conjunto de textos relacionados, para ser utilizado por um usuário particular e/ou para determinada tarefa. Com o crescimento exponencial das informações disponíveis e a necessidade das pessoas obterem a informação em um curto espaço de tempo, a tarefa de sumarização automática tem recebido muita atenção nos últimos tempos. Sabe-se que em um conjunto de textos relacionados existem informações redundantes, contraditórias e complementares, que representam os fenômenos multidocumento. Em cada texto-fonte, o assunto principal é descrito em uma sequência de subtópicos. Além disso, as sentenças de um texto-fonte possuem graus de relevância diferentes. Nesse contexto, espera-se que um sumário multidocumento consista das informações relevantes que representem o total de textos do conjunto. No entanto, as estratégias de sumarização automática multidocumento adotadas até o presente utilizam somente os relacionamentos entre textos e descartam a análise da estrutura textual de cada texto-fonte, resultando em sumários que são pouco representativos dos subtópicos textuais e menos informativos do que poderiam ser. A fim de tratar adequadamente a relevância das informações, os fenômenos multidocumento e a distribuição de subtópicos, neste trabalho de doutorado, investigou-se como modelar o processo de sumarização automática usando o conhecimento semântico-discursivo em métodos de seleção de conteúdo e o impacto disso para a produção de sumários mais informativos e representativos dos textos-fonte. Na formalização do conhecimento semântico-discursivo, foram utilizadas as teorias semântico-discursivas RST (Rhetorical Structure Theory) e CST (Cross-document Structure Theory). Para apoiar o trabalho, um córpus multidocumento foi anotado com RST e subtópicos, consistindo em um recurso disponível para outras pesquisas. A partir da análise de córpus, foram propostos 10 métodos de segmentação em subtópicos e 13 métodos inovadores de sumarização automática. A avaliação dos métodos de segmentação em subtópicos mostrou que existe uma forte relação entre a estrutura de subtópicos e a análise retórica de um texto. Quanto à avaliação dos métodos de sumarização automática, os resultados indicam que o uso do conhecimento semântico-discursivo em boas estratégias de seleção de conteúdo afeta positivamente a produção de sumários informativos.The multi-document summarization aims at producing a summary from a set of related texts to be used for an individual or/and a particular task. Nowadays, with the exponential growth of available information and the peoples need to obtain information in a short time, the task of automatic summarization has received wide attention. It is known that in a set of related texts there are pieces of redundant, contradictory and complementary information that represent the multi-document phenomenon. In each source text, the main subject is described in a sequence of subtopics. Furthermore, some sentences in the same text are more relevant than others. Considering this context, it is expected that a multi-document summary consists of relevant information that represents a set of texts. However, strategies for automatic multi-document summarization adopted until now have used only the relationships between texts and dismissed the analysis of textual structure of each source text, resulting in summaries that are less representative of subtopics and less informative than they could be. In order to properly treat the relevance of information, multi-document phenomena and distribution of subtopics, in this thesis, we investigated how to model the summarization process using the semantic-discursive knowledge and its impact for producing more informative and representative summaries from source texts. In order to formalize the semantic-discursive knowledge, we adopted RST (Rhetorical Structure Theory) and CST (Cross-document Structure Theory) theories. To support the work, a multi-document corpus was annotated with RST and subtopics, consisting of a new resource available for other researchers. From the corpus analysis, 10 methods for subtopic segmentation and 13 orignal methods for automatic summarization were proposed. The assessment of methods for subtopic segmentation showed that there is a strong relationship between the subtopics structure and the rhetorical analysis of a text. In regards to the assessment of the methods for automatic summarization, the results indicate that the use of semantic-discursive knowledge in good strategies for content selection affects positively the production of informative summaries

Exploring the subtopic-based relationship map strategy for multi-document summarization

In this paper we adapt and explore strategies for generating multi-document summaries based on relationship maps, which represent texts as graphs (maps) of interrelated segments and apply different traversing techniques for producing the summaries. In particular, we focus on the Segmented Bushy Path, a sophisticated method which tries to represent in a summary the main subtopics from source texts while keeping its informativeness. In addition, we also investigate some well-known subtopic segmentation and clustering techniques in order to correctly select the most relevant information to compose the final summary. We show that this subtopic-based method outperforms other methods for multi-document summarization and that achieves state of the art results, competing with the most sophisticated deep summarization methods in the area

Update on the Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Guideline of the Brazilian Society of Cardiology-2019

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