When a Computer Cracks a Joke: Automated Generation of Humorous Headlines
Mika Hämäläinen

Khalid Alnajjar

Faculty of Arts
University of Helsinkimika.hamalainen@helsinki.fi

Faculty of Arts
University of Helsinki
khalid.alnajjar@helsinki.fi

Abstract

Alnajjar, Leppänen, and Toivonen 2019), we aim to intertwine headlines, creativity and humor by proposing a novel
method for humor generation that is reasoned by the existing
theories on humor.
Our approach alters a word in an existing headline for ahumorous effect. We evaluate the method proposed in thispaper quantitatively with human judges. We take the different constituents of humor in consideration in the evaluation
to uncover the relation of each feature to the humor producedby our system.

Automated news generation has become a major interest for new agencies in the past. Oftentimes headlines for such automatically generated news articles areunimaginative as they have been generated with readymade templates. We present a computationally creative
approach for headline generation that can generate humorous versions of existing headlines. We evaluate oursystem with human judges and compare the results tohuman authored humorous titles. The headlines produced by the system are considered funny 36% of thetime by human evaluators.

Related Work
Humor has received some interest in the past for more thana decade (Ritchie 2005; Hong and Ong 2009; Valitutti et al.
2013; Costa, Oliveira, and Pinto 2015). We dedicate the remaining of this section to describing some of the most recent
work conducted on the topic.
Pun generation with a neural model language model is
one of the most recent efforts on humor generation (Yu,Tan, and Wan 2018). Their approach consists of training
a conditional language model an using a beam search to findsentences that can support two polysemous meanings for agiven word. In addition they train a model to highlight thedifferent meanings of the word in the sentence. Unfortunately, they evaluate their system on human evaluators basedon three quantitative metrics: fluency, accuracy and readability, none of which tells anything about how funny or apt
the puns were.
Alnajjar and Hämäläinen (2018) present a genetic algorithm approach for generating humorous and satirical movie
titles out of existing ones. Their method works on a wordlevel replacement and aims for low semantic similarity of
the replacement word with the original word to maximize
surprise and high similarity with Saudi Arabia to maximize
coherence. They consider pun as one of the fitness functions of the genetic algorithm, but the output is not strictly
limited to puns. On top the genetic algorithm, they train an
RNN model that learns from the genetic algorithm and real
people.
Surprise is also one of the key aspects of a recent pun
generator (He, Peng, and Liang 2019). They model surpriseas conditional probabilities. They introduce a local surprisemodel to assess the surprise in the immediate context of thepun word and a global surprise to assess the surprise in the

Introduction
Humor, while showcased by a wide spectrum of species
in the animal kingdom, has a subcategory that is exclusive
to the human being. Verbal humor can only exist in thepresence of language, and its generation by computational
means is far from trivial.
Humor is effectively a perceiver dependent phenomenon.
Nothing can be inherently funny, but humor is perceived andappraised by the mind of a human perceiving it. And ultimately, the perceived humor, if accepted as such by thelistener, elicits an emotional response accompanied with a
vocal response of laughter that originates from our ancestors, the species before homo sapiens (cf. Ross, Owren, andZimmermann 2010).
Our paper focuses on generating humor in news headlines. This NLG task is not of the traditional sort, whereconveying factual information is the uttermost goal of thesystem, but rather the affective content of the message istaken into the primary focus of the study.
Automated news generation is a flourishing field with new
research being published in a timely manner. This is reflected by the number of recent publications on the topic(Nesterenko 2016; Yao et al. 2017). Quite often, however,as the generated news has to cater for the purpose of communicating facts the question of creativity is set aside. In
such a context, there is a trade-off between creativity andcommunicativity (see Hämäläinen and Honkela 2019).
Whereas creative headline generation is not a new domain to computational creativity, with quite some existing
publications on the topic (Lynch 2015; Gatti et al. 2015;

Proceedings of the 12th International
Conference on Computational Creativity (ICCC ’21)
ISBN: 978-989-54160-3-5292

a collaboration, where the listener willingly accepts the humorous interpretation of the joke. Moreover, he argues thatwhile incongruity contributes to humor, it does not alone
constitute it.

context of the whole text. Their approach retrieves text froma corpus based on an original word - pun word pair. They
do a word replacement for local surprise and insert a topic
word for global surprise.
An approach building on humor theories is that of Winters, Nys, and De Schreye (2019). The theories are usedin feature engineering. They learn templates and metrical
schemata from jokes rated by people with a star rating. They
embrace more traditional machine learning techniques overneural networks, which has the advantage of a greater interpretability of the models.
Humor has also been tried to recognize automatically inthe past. One of such attempts is focuses on extracting
humor anchors, i.e. words that can make text humorous,
automatically (Yang et al. 2015). A similar humor anchor based approach is also embraced by Cattle and Ma(2018). Both of the approaches rely on feature engineering basing on humor theories. Recently LSTM models havebeen used for the task of humor detection with a different
rates of success (Cai, Li, and Wan 2018; Sane et al. 2019;
Zou and Lu 2019).

Generating Humorous Headlines

In their work, Hossain, Krumm, and Gamon (2019) identified several ways people altered news headlines to be humorous. In our method, we aim to model the following ones of
their findings: the replacement forms a meaningful n-gram,the replacements are semantically distant, the replacement
makes a strong connection with the entity of the headline
and belittles an entity or a noun and the replacement createsincongruity. We see the n-gram finding in a broader way of
the replacement being compatible with the the existing script(context). The semantic distance is seen as an index of surprise, and the connection between the entity is assimilatedwith the target of the joke.
The findings we are not focusing on in this paper are thatthe replacements are sarcastic, suppress tension or have asetup and punchline. The first two are left out as assessing
them computationally is a task worth of a paper on their own
right, and the third one is left out as it focuses on a particular kind of humor. However, the punchline structure might
emerge from the other features being modelled although not
explicitly taken into consideration.
In addition to the findings described above, we take theconcreteness of the replacement word into account. The reason for this that concrete words are more likely to provoke
mental images (see (Burroway 2007)). In fact, we could see
this in the humorous training dataset by Hossain, Krumm,
and Gamon (2019), where 90% of the most humorous replacement words were concrete as opposed to only 75% of
the least humorous replacement words being concrete.
For the above experiment and the rest of the paper, we use
the lexicon of 40k common English words that has a concreteness score from 1 to 5 assigned (Brysbaert, Warriner,
and Kuperman 2014). If the score assigned with the word isgreater or equal to 3, we consider it concrete. The concreteness is evaluated by lemmatizing the word with spaCy (Honnibal and Montani 2017) if it does not exist in the lexicon.

Humor
Humor is an inherent part of being a human and as such it
has provoked the interest of many researchers in the past to
formulate a definition for it (see (Krikmann 2006)). Koestler(1964) sees humor as a part of creativity together with discovery and art. In his view, what is characteristic to humor
in comparison to the other two constituents of creativity, isthat its emotional mood is aggressive in its nature. He calls
bisociation in humor the collision of two frames of reference
in a comic way.
Raskin (1985) presents a theory that is not too far awayfrom the previously described one in the sense that in orderfor text to be humorous, it has to be compatible with two
different scripts. The different scripts have to be somehow
in opposition, for example in the sense that one script is areal situation and the other is not real.
In Attardo and Raskin (1991) humor is seen to consistof six hierarchical knowledge resources: language, narrative
strategy, target, situation, logical mechanism and script opposition. As in the previous theories, the incongruity of two
possible interpretations is seen as an important aspect for
humor. An interesting notion that we will take into a closer
examination is that of target. According to the authors it is
not uncommon for a joke to have a target, such as an important political person or an ethnic group, to be made funof.
Two requirements have been suggested in the past as components of humor in jokes: surprise and coherence (see(Brownell et al. 1983)). A joke will then consist of a surprising element that will need to be coherent in the context
of the joke. This is similar to having two incongruous scripts
being simultaneously possible.
Veale (2004) points out that the theories of Raskin (1985)
and Attardo and Raskin (1991) entail people to be forced
into resolution of humor. He argues that humor should not
be seen as resolution of incompatible scripts, but rather as

Proceedings of the 12th International
Conference on Computational Creativity (ICCC ’21)
ISBN: 978-989-54160-3-5Modelling Humor
Our system operates by taking an existing headline from thecorpus of altered headlines (Hossain, Krumm, and Gamon2019). This corpus has been syntactically parsed by us byusing spaCy (Honnibal and Montani 2017), and it has beentagged for the words that should be replaced by its original
authors. For a selected headline, our system tries to findsuitable humoristic replacement words.
We assess the different potential humorous replacements
in terms of multiple parameters, which are prosody, concreteness, semantic similarity of the replacement to the original word and the semantic relatedness of the replacement tonegative words describing the target. In this section, we explain how the individual parameters are modelled. An overall view of our method is depicted in Figure 1.
For prosody, we look at the sound similarity between
the original word and the replacement. We assess this in293Figure 1: A diagram visualizing the process of humor generation.
terms of full rhymes, assonance, consonance and alliteration. These are implemented with rules. As the written formof English is notoriously deviant from the phonation, we use
eSpeak-ng1 to produce IPA transcription for the words the
prosody of which is being assessed.
For concreteness we use the values provided in Brysbaert,
Warriner, and Kuperman (2014) to score the concretenessof the replacement word. And for semantic similarity weuse the pretrained word embeddings from Bojanowski et al.
(2017). We use the semantic similarity to assess surprise,in other words, we want to minimize the similarity of thereplacement word to the original.
To measure how a new replacement connects to the word
selected to be the target of the joke in the headline, a target
must first be found. We consider recognized entities in theheadline as the potential targets. In case no entities wererecognized, we use the subjects in the headline. If neither ofthem existed, nouns in the headline are treated as target. Outof the list of targets, a random target t is picked to focus on.
For this target t, we retrieve words that are related to it to act
as descriptive words revealing potential attributes to makefun of. We employ two resources to obtain such knowledge
regarding the selected target:
1. The Non-Official Characterization (NOC) list (Veale
2016) which contains information about more than 1000
well-known characters (e.g. Donald Trump and Kim
Jung-un) and their expanded stereotypical properties supplied by (Alnajjar et al. 2017) (e.g. Donald Trump:[wealthy, successful, greedy, aggressive, . . . etc]).
2. A semantic relatedness model built from word associations collected from a web text corpus ukWac2 , following the approach described in Meta4meaning (Xiao et al.
122016). We chose to base our relatedness model on a webbased corpus instead of a news-based one to favor discovering related words from various domains, which would
be perceived as more humorous.
If the target t is an entity, we search the first resources (i.e.
the NOC list and the expanded properties) to collect its top
k stereotypical properties. In case no available knowledge
regarding the entity existed, we attempt to acquire the top
k related words to the rest of the potential targets (subjects
and nouns, respectively) using the second resource (i.e. thesemantic relatedness model). In our case, we empirically set
k to 100 to allow diversity and reduce noisy relations, while
ensuring the descriptiveness of the words to the target.
To be able to place the target in a humorous light, weonly regard the descriptive words that describe it negatively,
which is determined by employing a polarity classifier provided by Akbik, Blythe, and Vollgraf (2018). Lastly, theconnection of the replacement word to the target is assessed
based on the semantic relatedness between the replacement
word and the target’s negative descriptions. We desire to
maximize such connections to encourage replacements thatare associated with the target from a negative angle.

Generation and picking out the best candidate
We use the Humicroedit dataset of headlines published by
Hossain, Krumm, and Gamon (2019) as the source of original headlines. Furthermore, the dataset contains edits performed by humans to make the headlines humorous alongwith a score indicating how humorous they were when perceived by other people on a scale from 0 to 3. The motivation for using this dataset is that the editors were required tomake a single change to either a verb or a noun in the head-

UK English voice, https://github.com/espeak-ng/espeak-ng
https://wacky.sslmit.unibo.it/doku.php?

Proceedings of the 12th International
Conference on Computational Creativity (ICCC ’21)
ISBN: 978-989-54160-3-5

id=corpora294

c, and 4) the semantic relation between the candidate c andthe negative words of the selected target t. As we are dealing with multiple criteria for modeling humour, we adopt
a non-dominant multi-objective sorting approach (Deb et al.
2000) to find and select candidates in the Pareto front. Additionally, applying a non-dominant sorting for creative tasks(e.g. generating humour) increases the chances of findingbalanced and diverse solutions that are more likely to bedeemed good (Alnajjar, Hadaytullah, and Toivonen 2018).
Applying the evaluation and the non-dominant sortingon the example headline, the method highlights candidates
such as ‘cash’, ‘meal’, ‘drug’ to be chosen as replacements.
For the same example, the original word climate was replaced with marijuana by a human editor in the Humicroedit
dataset. Interestingly, marijuana is a drug and our method
was able to suggest it.

dobj

reads
VBZ

book
NN

187
Frequency

Figure 2: An example of a syntactical relation in the repository of grammatical relations (Alnajjar 2018) along with its
frequency.
line to make it humorous, which focuses the scope whenmodeling such a process computationally.
In our generation method, we only consider headlines
where the original word that is selected to be replaced isparsed as either a noun or a verb using spaCy and is a single token (i.e. ignoring cases such as “Illegal Immigrants”).
The rational behind is to reduce misparsing errors and concentrate on a single-word changes.
For an original headline ho with its selected word to be
replace wo , our method converts it into a humorous one hhby replacing wo with another word wh as follows. It begins
by acquiring replacement candidates C that fit the syntactical position of the selected word wo by querying a massive
syntactical repository of grammatical relations that have afrequency greater than 50 in a web-based corpus (Alnajjar
2018) (see Figure 2 for an example of a grammatical relation in the repository). By considering candidates that are
apt to the existing syntactical relations in the headline, we
ensure that the new replacement has syntactic cohesion andsuits the grammatical context.
To illustrate how the method works, let’s consider theheadline ho = “City halls and landmarks turn green in support of Paris climate deal” as an example, where the word to
replace wo = climate. After parsing this headline, we findthat the to-be-replaced word wo is a noun (NN) and has adependency (compound) on the word deal (NN). We query
the syntactical repertory to find potential replacements thatsuit this relation, which yields 58 candidate replacements(C ={‘loan’, ‘business’, ‘cash’, ‘oil’, ‘holiday’, ‘peace’,‘content’, ‘drug’ . . . etc}).
In the next phase, the method removes the original word
wo from the candidates if it existed and prunes out any candidate word in C that is not identified as concrete (i.e. having
a concreteness score greater or equal to 3 based on (Brysbaert, Warriner, and Kuperman 2014)). As a result, candidate words such as ‘peace’ and ‘content’ in the earlier example are removed resulting in a total of 34 candidates. If
there is more than 500 replacement candidates (e.g. in situations where the token to replace is a verb and is the rootof the phrase), we randomly select 500 candidates in C to
be examined. This is performed to reduce the search spacethat the method will traverse and to efficiently discover local optimal solutions as there is no particular global optimal
solution for the task we are addressing.
Replacement candidates are then evaluated on the four
humour aspects we are modeling, which are 1) prosody, 2)
concreteness score, 3) inverted (i.e. minimized) semantic
similarity between the original word wo and the candidate

Proceedings of the 12th International
Conference on Computational Creativity (ICCC ’21)
ISBN: 978-989-54160-3-5

Results and Evaluation
To evaluate our method, we randomly select 83 headlines
from the Humicroedit dataset that meet our criteria specified
earlier. For each headline, we request our method to produce
humorous alternatives, ranked by the non-dominant sorting,out of which we randomly select 3 to be evaluated from the
top humorous headlines.
Table 1 shows some of the headlines generated by ourapproach. The humorous replacement word is marked inbold. The original word and the replacement word suggested
by a human from the corpus are shown in their respective
columns.
We conduct our evaluation on Figure-Eight3 , which isa crowd-sourcing platform that assigns paid reviewers fortasks such as questionnaires. We evaluate all the 3 variations produced by our system for the 83 headlines, showing
the original headline as well. In addition, we evaluate thehuman edits for the same headlines from the dataset. The
reviewers were not told they were evaluating computer generated humor, as the mere knowledge of a computer being
an author of a creative artefact is known to provoke a bias
towards seeing the generated output in a more negative light(see (Colton, Wiggins, and others 2012)).
We asked five people to rate the headlines based on thefollowing questions:
1. The altered headline is humorous.
2. The altered word is surprising.
3. The altered word fits into the headline.
4. The altered word is concrete.
5. The joke of the headline makes fun of a person or a groupof people (also known as the target of the joke).
6. The altered word shows the target in a negative light.
7. The altered word is a pun of the original word.
We evaluate the first two questions on the scale form 0 to
3 (Not funny, Slightly funny, Moderately funny and Funny.
Or surprising in the case of the Q2) similarly to the questions for humor in Hossain et al. (2017). The rest of the3295

https://www.figure-eight.com/

Humorous headline by our system
Thieves carry out elaborate van heist to steal millions
in cereal, Swiss police say
Trump eats the wrong Lee Greenwood on Twitter’I was very angry’ at Trump, says Myeshia Johnson,
widow of fallen sock
Trump Tried To Climb Heather Heyer’s Mother
During Funeral: ‘I Have Not And Now I Will Not’
Talk To Him
U.S. says Turkey is helping ISIS by Combing
Kurds in Syria

Original word

Human replacement

cash

blouses

tags

woos

soldier

cake

call

proposition

bombingfeeding

Table 1: Examples of generated headlines.
questions are presented as yes/no questions. The sixth question is only visible if the fifth question has been answered toaffirmatively.

original headline, and min shows the average of the worst
headline in the triplets. Avg is the average of the scores for
all the generated headlines.
By looking at the results this way, we can see that at best,
our method can produce humor comparable to real humans
in the scale of funniness, with a higher amount of surprise,
better aptness of the replacement word to the context, higher
level of concreteness, higher negativity towards the target
and higher level of puniness, falling shorter only in the caseof having a perceivable target for the joke in the headline.
Focusing on the best scoring individuals might sound likegiving too good a picture of the performance of the system,
however, they set the upper boundary for the performanceof the system. This being said, with the exact same method,
better results could be obtained in the future by developinga better way for ranking the humorous headline candidates
output by the system.
By considering the headlines produced by our method thathave the maximum score for an original headline, we seethat 47 of them were credited as humorous (i.e. having ascore ≥ 1) out for the 83 original title. On the other hand,
43 of the human generated were considered humorous.
In the following analysis, we aim to evaluate the different
criteria considered in our method for modeling humour. In
terms of prosody, we look at the number of times a headline
was considered to be punny by people with respect to our
method’s score on the prosody dimension. Overall, 22% of
the generated headlines were considered to have a pun in relation to the original word. Out of these headlines, 88% ofthem were evaluated positively on the prosody dimension by
our system. This indicates that the method exhibited capability of assessing the sound similarity and punniness to theoriginal word.
For the concreteness, we are considering concrete words
defined in (Brysbaert, Warriner, and Kuperman 2014) as
candidates. As a result, we expected to have headlines produced by the method score high on the fourth question. Contrary, only 56% of them were deemed concrete. This indicates that a more robust model is required to model the concreteness of terms.
By observing Figure 3, we notice that 46% of headlines suggested by our method are considered surprising (i.e.
scoring at 1, on average). As we are using a word embeddings model, it is difficult to come up with a semantic

Figure 3: Percentages for each evaluation question. H marking human authored headlines, and S computer authored
ones.
Figure 3 shows the percentages of the result for each question from the human evaluation. The results for the humanedited titles (H) and the ones produced by our method (S)are shown side by side. From the question 3 onward, 0marks negative and 1 affirmative answer. All in all, our system scored slightly lower on the questions than real people,
which is to be expected due to the difficulty of the problem.
However, our system got slightly better results in the question number 6, which means, that when the system had recognizably picked a target, it managed to convey negativity
towards the target on a level comparable to a real human.
In terms of humor, our system managed to produce at leastslightly humorous headlines 36% of the time, whereas people produced at least slightly humorous headlines 56% of
the time. In comparison, for a recent pun generator, (He,Peng, and Liang 2019) report a success rate of 31% for theirsystem according to a human evaluation, to put our results
in a computational perspective.
Table 2 shows the results form another perspective. The
score row shows the results for human authored titles in theoriginal publication (Hossain et al. 2017), whereas the human row shows the results for the very same titles in ourevaluation. The max shows the average of the best scoring generated headline out of the 3 ones produced for each

Proceedings of the 12th International
Conference on Computational Creativity (ICCC ’21)
ISBN: 978-989-54160-3-5296

score
human
max
avg
min

Q1
µx
SD
0,99 0,62
0,97 0,49
0,97 0,45
0,6 0,28
0,28 0,27Q2µxSD0,89 0,41
0,98 0,36
0,67 0,23
0,37 0,24Q3µxSD0,58 0,23
0,69 0,19
0,53 0,15
0,36 0,18

Q4
µx
SD
0,6
0,2
0,7 0,15
0,56 0,11
0,4 0,16Q5µxSD0,36 0,23
0,33 0,17
0,19 0,12
0,06 0,12Q6µxSD
0,61 0,41
0,86 0,34
0,6 0,31
0,27 0,39

Q7µxSD
0,28 0,18
0,35 0,17
0,22 0,110,1 0,11

Table 2: Mean and standard deviation of altered headlines by humans and our method.
similarity threshold that separates similar words from nonsimilar ones, especially for modeling surprisingness. Therefore, we test the scores assigned by the models on three
thresholds of similarity (0.3, 0.2 and 0.1) with respect to theheadlines viewed as surprising by online people. Out of the
46% surprising headlines, 98%, 84% and 40% headlines are
considered to be dissimilar by the semantic model by using
the three above mentioned thresholds. This indicates thatminimizing the semantic similarity increases surprise to adegree, after which lowering the similarity results in a lower
surprise.
Lastly, we perform the same analysis regarding the connection between the replacement word and the selected target with respect to question five and six. 75% of the time,
our function scored positively on headlines evaluated as
making fun of a target. Out of which, 77% were correctly
seen as negative by the method with respect to Q6.

If the method was to be used as a tool for assisting journalists when composing news articles, the fact that employing
computational methods for headline generation might result
in offensive headlines (see (Alnajjar, Leppänen, and Toivonen 2019)) has to be taken into account. Our humor model
maximizes the negative relation to its target, which might
be considered as an insult, if understood in a wrong, nonhumorous fashion.
Our current approach focuses on English, in the future,
we are interested in using our method for other languages
as well such as Finnish. This would require a more robust
surface realization method to deal with morphology more
complex than that of English (Hämäläinen and Rueter 2018).
There is already a similar semantic database available for
Finnish (Hämäläinen 2018) as the one we used for English,
which greatly facilitates a multilingual port of our method.

Discussion

We have presented a method for generating humorous headlines that in its current state, falls behind the human level
humor. Nevertheless the results reach to a comparable levelwith an existing neural based method. The method proposedby us has the potential of reaching to a human level humor
generation in the limited domain task of altering a word inan existing headline if a better ranking mechanism for its
output was introduced.
The evaluation and analysis we conducted on the results
has revealed several features which can be modelled betterin the future to improve our method. As we have gathered
human judgements for headlines generated by our systemfor original headlines that are based on an existing humor
annotated corpus, we are releasing our evaluation results andthe generated titles4 in the same format as the corpus we
used so that our data can be easily used in research dealing
with the existing dataset.

Conclusions

As the best headlines produced by our system for each original headline can, on the average, reach to a human level interms of most of the factors measured by our evaluation, an
immediate future direction for our research is to develop abetter ranking mechanism to reach to the maximum capacity of our system. Perhaps such ranking could be learned
by training an LSTM classifier on humor annotated corpora
such as the one used in this paper or the one proposed by(West and Horvitz 2019).
For surprise, we opted for a rather modest approach byassimilating it to an inverse semantic similarity to the original word. However, different metrics have been proposed
to model this phenomenon, such as a neural network based
composer-audience model (Bunescu and Uduehi 2019) or
probabilistically modelling the likelihood of a certain word
occurring in a given context (see (Degaetano-Ortlieb andPiper 2019)).
The particularly low score on the concreteness highlights
the inadequacy of using an annotated lexicon for its assessment. Perhaps, in the future, concreteness could be modelled in a more robust context dependent way. Previous work
(Naumann, Frassinelli, and im Walde 2018) exists showing differences in the distributional representations of concrete and abstract words. As word embedding models are
based on the distributional hypothesis, this discovery could
be exploited for a context dependent classification by using
context-aware word embeddings.

Proceedings of the 12th International
Conference on Computational Creativity (ICCC ’21)
ISBN: 978-989-54160-3-5

Acknowledgments
This work has been partially supported by the European
Union’s Horizon 2020 programme under grant 825153 (Embeddia).

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