Text2Story

About Project

Extracting journalistic narratives from text and representing them in a narrative modeling language

Nowadays, journalistic content is distributed in multiple formats, mostly through the web and specific internet-based applications running on smartphones and tablets. Text is a very important format, but readers (or more accurately users or information consumers) heavily rely on images, videos, slideshows, charts and infographics. Textual content is still the main representation for information. Any journalistic subject (e.g. Trump and Russia) is described in one or more texts produced by journalists and possibly commented by readers. Many of those subjects are followed during days, weeks or months. To grasp a possibly vast and somewhat complex set of interconnected news articles, readers would greatly benefit from tools that summarize those articles by showing main actors, their interplay and their trajectories in time and space, their motivations, main events, causal relations of events and outcomes. In the Text2Story project we use Artificial Intelligence, Computer Science and Linguistics to automatically extract those narrative elements using a well-defined semantic framework and re-represent them in formats that convey the essential story but that are more efficiently consumed by the users. The project is lead by INESC TEC in collaboration with researchers from the Center of Linguistics of U. Porto, the Lusa News Agency and Jornal Público.

Acronym

Text2Story

Responsible

Alípio Mário Guedes Jorge

Status

Concluded

Start

January 14, 2019

End

January 30, 2023

Effective End

January 30, 2023

Global Budget

€239,736.00

Financing

€219,852.00

Website

Members

Team Leaders

Alípio Jorge

I am an associate professor at the Department of Computer Science of the Faculty of Science of the University of Porto and the coordinator of LIAAD , the Artificial Intelligence and Decision Support Lab of UP. LIAAD is a unit of INESC TEC (Laboratório Associado) since 2007. I am a PhD in Computer Science by U. Porto, MSc. on Foundations of Advanced Information Technology by the Imperial Collegeand BSc. in Applied Maths and Computer Science, currently Computer Science (U. Porto). My research interests are Data Mining and Machine Learning, in particular association rules, web and text intelligence and data mining for decision support. My past research also includes Inductive Logic Programming and Collaborative Data Mining. I lecture courses related to programming, information processing, data mining, and other areas of computing. While at the Faculty of Economics, where I stayed from 1996 to 2009, I launched, with other colleagues, the MSc. on Data Analysis and Decisison Support Systems, which I coordinated from 2000 to April 2008. I lead research projects on data mining and web intelligence. I was the director of the Masters in Computer Science at DCC-FCUP from June 2010 to August 2013. I co-chaired international conferences (ECML/PKD 2015, Discovery Science 2009, ECML/PKDD 05 and EPIA 01), workshops and seminars in data mining and artificial intelligence. I was Vice-President of APPIA the Portuguese Association for Artificial Intelligence.

Team Members

Hugo Oliveira Sousa

Ricardo Campos

Evelin Freire Amorim

Sérgio Nunes

Alípio Jorge

Associated Centres

Artificial Intelligence and Decision Support

Our Laboratory of Artificial Intelligence and Decision Support (LIAAD) conducts research in the fields of Artificial Intelligence, Machine Learning, Data Science, and Modelling. These areas are cross-cutting and apply to all sectors of society and the economy. The vast amounts of data being collected, alongside the ubiquity of digitalisation and sensorisation, are increasingly creating opportunities and challenges for automating decision support. The combination of Machine Learning and complex models is transforming the economy, healthcare, justice, industry, science, public administration, and education. This encourages us to invest in diverse technological and scientific approaches and perspectives. Our overarching strategy is to explore the flow and diversification of data, and to invest in research lines that will lead to the development of applied Artificial Intelligence foundations and models that are responsible and human centred.