Abstract
Event extraction (EE) is one of the core information extraction tasks, whose purpose is to automatically identify and extract information about incidents and their actors from texts. This may be beneficial to several domains such as knowledge base construction, question answering and summarization tasks, to name a few. The problem of extracting event information from texts is longstanding and usually relies on elaborately designed lexical and syntactic features, which, however, take a large amount of human effort and lack generalization. More recently, deep neural network approaches have been adopted as a means to learn underlying features automatically. However, existing networks do not make full use of syntactic features, which play a fundamental role in capturing very long-range dependencies. Also, most approaches extract each argument of an event separately without considering associations between arguments which ultimately leads to low efficiency, especially in sentences with multiple events. To address the above-referred problems, we propose a novel joint event extraction framework that aims to extract multiple event triggers and arguments simultaneously by introducing shortest dependency path in the dependency graph. We do this by eliminating irrelevant words in the sentence, thus capturing long-range dependencies. Also, an attention-based graph convolutional network is proposed, to carry syntactically related information along the shortest paths between argument candidates that captures and aggregates the latent associations between arguments; a problem that has been overlooked by most of the literature. Our results show a substantial improvement over state-of-the-art methods on two datasets, namely ACE 2005 and TAC KBP 2015.

Abstract

Abstract

Abstract
Over the past few years, the amount of information generated, consumed and stored on the Web has grown exponentially, making it impossible for users to keep up to date. Temporal data representation can help in this process by giving documents a sense of organization. Timelines are a natural way to showcase this data, giving users the chance to get familiar with a topic in a shorter amount of time. Despite their importance, little is known about their use in the context of single documents. In this paper, we present Time-Matters, a novel system to automatically explore arbitrary texts through temporal narratives in an interactive fashion that allows users to get insights into the relevant temporal happenings of a story through multiple components, including temporal annotation, storylines or temporal clustering. In contrast to classical timeline multi-document summarization tasks, we focus on performing text summaries of single documents with a temporal lens. This approach may be of interest to a number of providers such as media outlets, for which automatically building a condensed overview of a text is an important issue. © 2021, Springer Nature Switzerland AG.

Abstract
The rise of social media and the explosion of digital news in the web sphere have created new challenges to extract knowledge and make sense of published information. Automated timeline generation appears in this context as a promising answer to help users dealing with this information overload problem. Formally, Timeline Summarization (TLS) can be defined as a subtask of Multi-Document Summarization (MDS) conceived to highlight the most important information during the development of a story over time by summarizing long-lasting events in a timely ordered fashion. As opposed to traditional MDS, TLS has a limited number of publicly available datasets. In this paper, we propose TLS-Covid19 dataset, a novel corpus for the Portuguese and English languages. Our aim is to provide a new, larger and multi-lingual TLS annotated dataset that could foster timeline summarization evaluation research and, at the same time, enable the study of news coverage about the COVID-19 pandemic. TLS-Covid19 consists of 178 curated topics related to the COVID-19 outbreak, with associated news articles covering almost the entire year of 2020 and their respective reference timelines as gold-standard. As a final outcome, we conduct an experimental study on the proposed dataset over two extreme baseline methods. All the resources are publicly available at https://github.com/LIAAD/tls-covid19. © 2021, Springer Nature Switzerland AG.

Abstract
Narrative extraction, understanding and visualization is currently a popular topic and an important tool for humans interested in achieving a deeper understanding of text. Information Retrieval (IR), Natural Language Processing (NLP) and Machine Learning (ML) already offer many instruments that aid the exploration of narrative elements in text and within unstructured data. Despite evident advances in the last couple of years the problem of automatically representing narratives in a structured form, beyond the conventional identification of common events, entities and their relationships, is yet to be solved. This workshop held virtually onApril 1st, 2021 co-located with the 43rd European Conference on Information Retrieval (ECIR’21) aims at presenting and discussing current and future directions for IR, NLP, ML and other computational fields capable of improving the automatic understanding of narratives. It includes a session devoted to regular, short and demo papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of the area. © 2021, Springer Nature Switzerland AG.