BLAB Reporter: Automated journalism covering the Blue A mazon (2022)

Abstract: This demo paper introduces BLAB reporter, a robot-journalist system covering the Brazilian Blue Amazon. The application is based on a pipeline architecture for Natural Language Generation, which offers daily reports, news summaries and curious facts in Brazilian Portuguese. By collecting, storing and analysing structured data from publicly available sources, the robot-journalist uses domain knowledge to generate, validate and publish texts in Twitter. Code and corpus are publicly available.

## introduction data-to-text natural language generation (nlg) is the computational process of generating meaningful and coherent natural language in the form of text or speech to describe non-linguistic input data @xcite . successful examples of data-to-text systems can be found in both academia and industry, with applications in weather forecasting @xcite , image captions and chatbots @xcite . within the range of nlg applications, robot-journalism is one of the most prominent endeavors thanks to the high volume of structured data streams available, which enables automated systems to report recurrent information with high-fidelity and lexical variety @xcite ). an interesting domain for data-to-text generation is ocean monitoring. for instance, global attention was drawn in 2021 to a container ship that obstructed the suez canal for six consecutive days. the result was a global shortage of essential commodities, including medical supplies and medicines, which were essential during the coronavirus pandemic . accurate and low latency information reports can be very helpful in these situations, but communicating to general audiences in a accessible way usually demands coverage by specialized human journalists. to address this issue, we present our robot-journalist named blab reporter, a nlg system based on a pipeline architecture that generates daily reports, news, content summarization and curious facts about the blue amazon and publishes them on twitter in brazilian portuguese 2 . the blue amazon is the exclusive economic zone (eez) of brazil, with an offshore area of 3.6 million square kilometers along the brazilian coast, an area rich in marine biodiversity and energy resources @xcite . the blue amazon brain (blab) is a project aiming to address complex questions about the marine ecosystem, and integrates a number of services aimed at disseminating information about the blue amazon region and its importance. ## system overview our system follows a pipeline architecture that converts non-linguistic data into text in 6 steps: content selection, discourse ordering, text structuring, lexicalization, referring expression generation and textual realization @xcite . our system also comprises two additional steps: data acquisition, responsible for extracting and storing information from multiple data streams in a structured format, and summarization, responsible for summarizing news in the form of small consecutive tweets. this kind of architecture, depicted in figure 1 , allows for trustworthy output as well as easy access to and maintenance of sub-modules. the grammar used by the model was built by first running the content selection step in previous data and generating 30 non-linguistic reports. these non-linguistic reports were then manually verbalized and the input and output representations for each pipeline module were manually annotated. when deployed, each module draws on the selected combination of templates using rule-based approaches. because we deal with a sensitive do-2 https://twitter.com/blab_reporter 1 figure 1 : robot-journalist system architecture main, we opted to use the pipeline architecture instead of the novel end-to-end systems, which sometimes hallucinates content @xcite . the following sections describe each module. ## conclusions this paper presents a data-to-text system based on a pipeline architecture for nlg. our system applies robot-journalism techniques to generate and publish reports, news and curious facts in brazilian portuguese about the blue amazon. due to its rule-base nature, our system provides high-fidelity content by applying a pipeline methodology and obtains lexical variety by drawing from a list of multiple available template options for the same intent. in the future we plan to add more sources of information to the pipeline, for example statistics about oil exploration and reporting of illegal fishing activities in real time. we also plan to utilize user engagement data and apply artificial neural network techniques to improve our system's performance.