Amplifying the Range of News Stories with Creativity: Methods and their Evaluation, in P ortuguese (2020)

Abstract: Headlines are key for attracting people to a story, but writing appealing headlines requires time and talent. This work aims to automate the production of creative short texts (e.g., news headlines) for an input context (e.g., existing headlines), thus amplifying its range. Well-known expressions (e.g., proverbs, movie titles), which typically include word-play and resort to figurative language, are used as a starting point. Given an input text, they can be recommended by exploiting Semantic Textual Similarity (STS) techniques, or adapted towards higher relatedness. For the latter, three methods that exploit static word embeddings are proposed. Experimentation in Portuguese lead to some conclusions, based on human opinions: STS methods that look exclusively at the surface text, recommend more related expressions; resulting expressions are somewhat related to the input, but adaptation leads to higher relatedness and novelty; humour can be an indirect consequence, but most outputs are not funny.

## introduction each minute, new stories are published online, listed in news aggregators, and spread through social media. with the amount of information each of us is constantly bombed with, most people end up looking at the headlines and, only sporadically, opening and reading the full text. we may thus say that headlines play a key role in this process: the more appealing they are, the higher the probability of someone actually reading their story. and while personal preferences are relevant, creativity and familiarity (e.g., text resembles a funny saying or situation) certainly contribute to higher appeal. a common strategy for making the headline more catchy is to reuse expressions known by a general audience, sometimes also attempting at a humorous effect. if the expression is related enough, it can be used directly, but it may also suffer minor adaptations, to become more related to the context and still resemble the original saying. this is also common in news satire tv shows, like the daily show. while the host is telling a story, in one of the top corners of the screen, a short text (e.g., a book, movie title, proverb, idiomatic expression, or their adaptation), often accompanied by an image, complements the scene. however, writing catchy headlines requires time and talent. even if resorting to well-known expressions is a possible shortcut, it is still a knowledge intensive task (i.e., on folk or pop culture) and requires creativity skills. therefore, in this paper, we propose to automate this process and assess different unsupervised methods for, given a short text (e.g., a news headline): (i) recommending a related known expression (e.g., a proverb) from a set; (ii) adapting a known expression so that it becomes related to the input. in any case, it should be possible to use the resulting expression as an alternative, though more creative, headline, sub-title, or, at least, a comment in social media. methods tested for recommendation are mostly baselines for semantic textual similarity (sts) @xcite , including some based on the surface text, others based on static word embeddings, and on bert @xcite contextual embeddings. as for the adaptation, static word embeddings are exploited for replacing some words in the expressions, taken directly from the headline, or based on similarity or analogy. proposed methods were tested with a set of news headlines, as input text, and a set of proverbs and movie titles. we have worked with portuguese, and even though the resources and tools used are for the target language, theoretically, the methods are language-independent. the development of automatic approaches for producing creative artefacts, sometimes based on a given context or cur-rent events, is not new. the proposed adaptation methods have some novelty, when compared to the well-established methods for recommendation. however, here they are used with proverbs, which poses additional challenges, such as the frequent utilisation of figurative language. finally, we target portuguese, a language for which work of this kind is still scarce. for better understanding how successful this approach was, results of this portuguese instantiation were assessed. due to the underlying subjectivity in the appreciation of the results, evaluations (one for recommendation, another for adaptation) are based on the opinion of human judges, who scored pairs of headlines and expressions by different methods. this further enabled to compare methods and draw some conclusions, such as: on average, results of recommendation methods are not significantly different from each other, but using expressions that share words with the headline seems to increase the perceived relatedness; resulting expressions are somewhat related to the input, and adaptation leads to higher relatedness and novelty; humour can be an indirect consequence, but most outputs are not funny. in the remainder of this paper, we overview work on the generation of creative text, focusing on those inspired by news or current events. we then describe our approaches for amplifying news stories with creative text and their evaluation: first, for the recommendation of known expressions, including the comparison of different methods; second, we propose three methods for adapting such expressions. we conclude with a brief discussion and some cues for future work. ## related work computational creativity (cc) is at the intersection of artificial intelligence and scientific areas like cognitive psychology, philosophy, and the arts. one of its ends is to develop creative systems, i.e., computational systems that exhibit behaviours deemed as creative by unbiased observers @xcite , e.g., they can produce new music or visual art, among others, including text. work on linguistically-creative systems has tackled the generation of textual artefacts like narrative @xcite , poetry @xcite @xcite , memorable news headlines @xcite @xcite and slogans @xcite , or humour @xcite . among those, some were adapted for producing new textual content based on current events. poetry generation has been: guided by dependency relations in a single news story @xcite ; inspired by the mood and related similes in a set of news stories, possibly including sentences from one of them @xcite ; or inspired by twitter trends, associated words and semantic relations @xcite . work on increasing the creativity, and thus memorability, of news headlines often resorts to wellknown expressions, with which readers are familiar, including slogans, movie or song titles. on this context, the creativity of an automated journalism system was increased with the injection of known expressions and figurative language @xcite , e.g., the headline "biggest gains for the christian democrats across lapin vaalipiiri" may become "biggest gains for the christian democrats, as powerful as a soldier, across lapin vaalipiiri'. human-written headlines were also blended with well-known expressions, through the substitution of words in the expression with keywords from the headline @xcite , e.g., "what the euro is coming to" for the original headline "uk anger at 1.7bn eu cash demand". to reduce the risk of producing outputs with a detached meaning, a threshold was set on the cosine similarity between the original and the produced headline. also relying on vector semantics and on the cosine, previously generated metaphors have been paired with the current news @xcite , e.g., the news tweet "@newtgingrich says 'the country will become enraged' if the violent protests at @realdonaldtrump rallies continue" was paired with metaphors like what is a radical but a crusading demagogue?. when it comes to the generation of creative text, word embeddings are useful resources. in fact, the operations of similarity, neighborhood, theme and analogy have been formalised for adapting text by lexical replacement, with increased creativity, given a set of intentions @xcite . in our work, we also propose methods for adapting wellknown expressions, based on a textual input (context), more like @xcite , with word embeddings exploited in the selection of suitable replacements. although any text should work, news headlines were also used. humour can be a consequence of the former approaches, but it is rarely tackled specifically. there is, however, work on humour generation, also relying on lexical substitution in short texts @xcite . in this case, messages, e.g., "later we go where to eat?" may become "later we go where to shit?". replacement words should have the same part-of-speech of the original, a similar sound or writing and, the more efficient constraint, is that they are taboos. @xcite present a corpus of original news headlines and their manuallyedited funny versions. while such a corpus may be useful for many tasks, including humor generation, funny headlines are often out-of-context (e.g., "eu says summit with turkey provides no answers to concerns" becomes "eu says gravy with turkey provides no answers to concerns"), which is different from our goal. another important difference of our work is that we experiment and present results for the portuguese language, while the majority of the aforementioned systems processes and produces english text. for portuguese, related work is less, but still covers: the automatic generation of poetry @xcite ; riddles @xcite , with the help of lexical-semantic knowledge; and memes @xcite , based on current news and rules for selecting an image macro and adapting the text. a related task is that of headline generation for a given document (see e.g., @xcite ), which has some similarities with automatic summarization @xcite and may involve a deeper understanding of the news story. this is, however, different from our goal, as our starting point are existing headlines, for which new creative alternatives are recommended or produced. alternatively to text generation, some systems simply recommend (famous) quotes @xcite , or retrieve interactions in movie subtitles @xcite , to be used in dialogues. this may involve training an encoder-decoder network @xcite in dialogues where target quotes are used. though, an unsupervised retrievalbased approach is also possible, e.g., relying on vector semantics and pretrained language models for computing semantic textual similarity @xcite , and retrieving the most similar quote. in this work, we also test different unsupervised methods for the direct recom-mendation of well-known expressions. ## recommendation of creative text in context the first part of this work tackled the recommendation of suitable expressions for a given short text. a good example of our goal is taken from the 17th may 2020 edition of the portuguese satire news tv show isto é gozar com quem trabalha: when presenting a story about the plan of the portuguese president to take a swim in the sea after the covid-19 lockdown, they used the text "o velho e o mar" (the old man and the sea, a book by ernest hemingway). for this, we tested different methods for computing the semantic similarity between news headlines, the input text, and portuguese proverbs, the expressions to output. all methods are unsupervised and can be seen as baselines for sts. once all available proverbs are ranked according to the input, the first, which maximises similarity, is recommended. this section describes the tested methods, how they were used in this experimentation, their results and evaluation by human judges. ## adaptation of expressions to a context according the previous evaluation, it is not easy to find suitable proverbs for a given headline. this could possibly be improved if the corpus of proverbs were increased. however, headlines can be significantly different and, virtually, on any topic, so another option is to start from any known expression and adapt it, so that it becomes more related to the headline, while still resembling the original expression. this is not new and is also a commonly adopted strategy in news satires. for instance, in the 5th april 2020 edition of isto é gozar com quem trabalha, the expression "droga de elite" (elite drug), an adaptation of the brazilian movie title "tropa de elite" (elite squad), illustrated a story on covid-19 in the brazilian favelas, where drug dealers were ensuring that residents followed the sanitary rules. this section describes three methods that explore static word embeddings in the automatic adaptation of a given expression, to suit, as much as possible, an input short text, in such a way that it can be used for transmitting or complementing the same idea, though more creatively. after presenting the methods, we describe an experiment where portuguese proverbs and movie titles were adapted for news headlines, followed by their results and evaluation. ## conclusions aiming at the amplification of news stories, we explored a set of automatic methods for making headlines more creative and appealing, with the exploitation of well-known expressions and word embeddings. when tested with portuguese news headlines, human opinions suggest that our goals were achieved with relative success. results are somewhat related to input headlines, especially if known expressions are adapted, and not used directly, which also increases novelty, intimately related to creativity. for the best adaptation methods, about a third of the results was clearly related to the headline. although, in a few cases, humour was an indirect consequence, most of the outputs were not so funny. in the future, funniness may benefit from ranking candidate results with a humour classifier, based on different humour-relevant features, such as the one recently proposed for portuguese @xcite . all the methods are unsupervised and exploring supervision was never our goal, mainly because the lack of training data, e.g., different portuguese headlines for the same news scored according to their creativity, possibly also including some created specifically for this purpose. while such a dataset is not available for portuguese, for english, a crowdsourced corpus of 15k original news headlines and their manually-edited funny versions was recently presented @xcite , making thus room for a data-driven approach for our task. another observation of our work was that, in this context, methods that consider exclusively the surface text retrieve proverbs that are perceived to be more related. this happens because recommended proverbs tend to use some words of the input, immediately suggesting some relation. although state-of-the-art bert embeddings would be better at representing sentence meanings, they are not apt to deal with the figurative language in proverbs, at least if they are not fine-tuned for this, or possibly trained only for the target language. in the future, it would be interesting to make a deeper study on sts and figurative language. the proposed methods can be integrated in a tool for suggesting alternative headlines that are still related to a story, possibly useful for journalists. even if not all results have the necessary quality, different options may be produced (e.g., the top-n for each method), faster than if it were a human, who may still make the final decision or additional adaptations. to some extent, enabling human interaction would make such a tool co-creative, as other interactive systems developed for writing stories @xcite , song lyrics @xcite or poetry @xcite , among others. finally, this work should also contribute to the development of more creative chatbots, e.g., capable of providing creative responses to out-ofdomain interactions. in the meantime, the teco twitterbot 7 is already following several portuguese newspapers and retweeting some of their publications together with comments produced by the proposed methods.