# Abstractive Summarizers Become Emotional on News Summarization
## 2 Related Work  
Automatic summarization has been addressed in the literature using mainly extractive or abstractive approaches. Extractive approaches build summaries by selecting text directly from the document [16; 17; 18], while abstractive systems build the summaries by paraphrasing text from the document [19; 20]. Recently, strong efforts have been made in developing abstractive systems by focusing on encoder-decoder architectures pre-trained in self-supervised ways [13; 14; 15]. One of the best-known problems of these systems is related to hallucinating content, where the models are prone to generate content in the summaries that is not directly inferable from the source document. Several works aim to reduce hallucinations or improve the factual consistency of abstractive summarizers, e.g., employing content planning [21], reinforcement learning [22], or constraining the generation [23]. Abstractive summarizers could also be guided, for instance, to work better on aggregating semantic information [8], with specific topics [24], or to represent better the keywords and relationships among the entities [25; 26].  
Along with hallucinations, factuality, and abstractivity, emotions are also important to be studied in summarization systems and in the corpora used to train them. Since summaries are an entry point to the source article, the emotions elicited in the summaries directly impact the perception of the users. Few works have considered emotions for  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Article** & Penglais Farm (Aberystwyth University) will have a total of 1000 rooms, but only 700 will be ready [anticipation] this month to welcome [joy] students. The university said developer Balfour Beatty confirmed [trust] the remaining 300 rooms will be ready [anticipation] during the 2015-2016 academic year. [PERSON] has been asked to comment. The unfinished [\(\
eg\)anticipation] rooms have not been let to students. \\ \hline
**Summary\({}_{1}\)** & Hundreds of rooms at a student halls development at Aberystwyth University will not be ready [\(\
eg\)anticipation] for the new term. \\ \hline
**Summary\({}_{2}\)** & 700 rooms at Aberystwyth University will be ready [anticipation] to welcome [joy] students this month. \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of two different summaries for the same article. Using the Nrc lexicon, we highlight the words that convey emotions (the emotions are listed in brackets). Phrases and emotions in blue refer to positive aspects, and those marked in red to negative aspects.  
summarization in dialogue or microblog summarization [5; 6], but, to our knowledge, only [1] has studied emotions in automatic news summarization. They proposed an emotion-aware news summarization system and introduced the concepts of emotion densities and ratios, which we used extensively in our work. Similarly, in our work, we use them to study salient emotions in human-written summaries of two widely used summarization corpora (CNN/DailyMail and XSUM). Different from [1], we also study the emotional behavior of abstractive summarization systems, and we do not ground emotions to predefined categories since (i) articles from the considered categories are discarded, (ii) current summarization corpora do not consider categories, and (iii) we aim to obtain global insights of emotions at newspaper-level.  
Emotions have been studied out of the scope of news summarization, to understand the affective state of users in applications such as e-commerce [27], opinion analysis in social media [28; 29], or healthcare [30; 31]. Emotions have also been studied in the news domain to detect fake news [32] or the stance toward specific targets [33]. To our knowledge, our work is the first to analyze emotions under the umbrella of news automatic summarization to obtain insights from the emotional content of news summarization corpora and the emotional behavior of abstractive summarizers.