Paula Christina Figueira Cardoso

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.

A Qualitative Analysis of a Corpus of Opinion Summaries based on Aspects

Aspect-based opinion summarization is the task of automatically generating a summary 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 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 sentiment 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.

An Open Source Tool for Crowd-Sourcing the Manual Annotation of Texts

Manually annotated data is the basis for a large number of tasks in natural language processing as either: evaluation or training data. The annotation of large amounts of data by dedicated full-time annotators can be an expensive task, which may be beyond the budgets of many research projects. An alternative is crowd-sourcing where annotations are split among many part time annotators. This paper presents a freely available open-source platform for crowd-sourcing manual annotation tasks, and describes its application to annotating causative relations.