RepLab Summarization Dataset This package contains the dataset generated in the research published in the paper: "Javier Rodríguez-Vidal, Jorge Carrillo-de-Albornoz, Enrique Amigó, Laura Plaza, Julio Gonzalo and Felisa Verdejo. 2019. Automatic Generation of Entity-Oriented Summaries for Reputation Management. Ambient Intelligence & Humanized Computing." The dataset is available for research purpose. If you use it, please, cite us. This README file contains: 1) A brief description of the corpus 2) A description of the contents of each directory in this package. 1. Description of RepLab Summarization Dataset The RepLab summarization dataset contains companies data from the RepLab 2013 dataset (http://nlp.uned.es/replab2013/), where users from Twitter talk about different topics of the companies. Each topic consists of a different number of tweets posted by Twitter users. The collection comprises tweets about 31 entities from two domains: automotive and banking. As a result, our subset of RepLab 2013 comprises 71,303 English and Spanish tweets For each entity, tweets are groupped in topics and for each topic three different summaries are manually generated: abstractive english, abstractive spanish and extractive. Please see the paper for further details. 2. Description of the contents of this package ./entities: This directory includes the information of each organization in order to create a summary. Each .xml file corresponds to an entity and includes the following information: -”Corpus entity”: Id of the entity. -”cluster”: each one of the topics of the entity. -"label": name of the topic. -"priority": level of relevance of the topic: Alert (the highest priority being a reputation alert, i.e., an issue that requires an immediate response from the entity), Midly_important (relevant for the entity, an intermediate priority) or unimportant (the lowest priority). -”tweet”: Information about the tweets. -"id": Id of the tweet. -"date": When the tweet was written. -"followers": Of the author of the tweet. -"polarity": Of the tweet. -"text": Text of the tweet. -"summary": Information about the summary: -"abstract_EN": Abstractive summary in English. -"abstract_ES": Abstractive summary in Spanish. -"tweet": Id of the tweet(s) selected for the extractive summary (if it is not filled, the extractive summary is the one of the tweets in the topic).