The important mass of textual documents is in perpetual growth and requires strong applications to automatically process information . Automatic titling is an essential task for several applications : ' No Subject ' e-mails titling , text generation , summarization , and so forth . This study presents an original approach consisting in titling journalistic articles by nominalizing . In particular , morphological and semantic processing are employed to obtain a nominalized form which has to respect titles characteristics ( in particular , relevance and catchiness ) . The evaluation of the approach , described in the paper , indicates that titles stemming from this method are informative and/or catchy . A title establishes a link between a reader and a text . It has two main functions . First of all , a title can be informative ( it conveys relevant information about the text content and aim ) , and second , it can be catchy or incentive ( Herrero Cecilia , 2007 ) . A heading is said to be catchy when it succeeds in capturing the reader 's attention on an aspect of the announced event , in a ingenious , metaphoric , enigmatic , or shocking way . From a syntactic point of view , a title can be a word , a phrase , an expression , a sentence , that designates a paper or one of its parts , by giving its subject . Titles are used within applications such as automatic generation of contents , or summarization . So , it is interesting to automate the process that produces relevant titles by extracting them from texts , and supplying other applications with such data , while avoiding any human intervention : Direct applications ( as automatic titling of " no object " e-mails ) are thus possible . The point is that several titles can be relevant for a same text : This constitutes the main difficulty of automatic titling . Some writers prefer informative titles , whereas others prefer catchy ones . Others juggle with both criteria according to the context and the type of the publication . So , evaluation of automatic titling is a complex step requiring a human intervention . Indeed , how can titles relevance be estimated ? How an automatic title can be compared to a human-written ( " real " ) title , knowing that both can have a very different morphosyntactic structure ? Automatic titling is a full process , possessing its own functions . It has to be sharply differentiated from summarization and indexation tasks . Its purpose is to propose title(s ) that have to be short , informative and/or catchy , and keep a coherent syntactic structure . NLP1 methods will be exploited in order to abide by language morphosyntactic and semantic constraints in titling . In this paper , we describe an approach of automatic titling relying on nominalization , i.e. rules transforming a verb phrase into a noun phrase ( e.g. " the president left " is nominalized into " President 's Departure " ) . This study raises two crucial questions : ( 1 ) Determining sentences and phrases containing relevant information ( 2 ) Nominalizing a chosen item and using it as a title . Example : From the following pair of sentences " The disappointing perfor-mance , on Sunday October 9th , of Ségolène Royal , amazed the French citizens . For months , they defended their candidate on the Web . " , containing the relevant information about an article in the French press in 2007 , the idea is to built the following title : " Ségolène Royal : Surprise of the French citizens " . In fact , other titles could apply such as " Ségolène Royal 's Disappointing Performance " or " Surprising the French Citizens " , but notice that both are less informative , since they drop a part of the information . This article is organized as such : The following section briefly positions automatic titling in its research environment and describes previous work ( section 2 ) . The next one describes NOMIT , our approach of automatic titling by nominalization , which consists in three successive steps : Extracting candidate headings from the document ( section 3.1 ) , processing them linguistically ( section 3.2 ) , and last , selecting one among the produced headings , which will play the role of the system heading suggestion ( section 3.3 ) . Finally , the results of NOMIT evaluation are presented and discussed ( section 4 ) . Automatic titling is a complex task because titles must be at once informative , catchy , and syntactically correct . Based on linguistic and semantic treatments , our approach determines titles among which approximately 80 % were evaluated as relevant and more than 60 % were qualified as catchy . Experiment and results discussion have pointed at the following liability : The value of Kappa , the inter-judges agreement coefficient , is very difficult to evaluate , mostly when catchiness is at stake . The main cause is that it depends on personal interests . It is thus necessary to ask the following question : Do we have to consider that a title is definitely catchy when at least one person judges it so ? Otherwise , how many people at least ? This is still an open question and needs to be further investigated . Also , some interesting extensions could be envisaged : The approach presented in this paper uses three rules of transformation based on the presence of an auxiliary followed by a past participle . The addition of new rules would enable a syntactic enrichment of the titles . So , it might be profitable to set up rules taking into account the presence of syntactical patterns ( others than " auxiliary + past participle " ) to allow more texts to be titled by NOMIT . Taking the punctuation of the end of sentences into account might also be a promising track . For example , " did it use an electric detonator ? " would become " Use of an electric detonator ? " . It is an interesting point because the presence of a punctuation at the end of a title ( in particular the exclamation or the interrogation ) constitutes a catchy criterion . Last , NOMIT is a method ( easily reproducible in other languages , English in particular ) that stepped out of preceding attempts in automatic headings generation ( POSTIT , CATIT ) . Exploring syntactic patterns , as it does , means that increasing the amount of linguistic information in the process might lead to a reliable heading method . One of the perspectives can be to track the optimum point between the richness of involved information and processes , and the cost of the method . The incremental methodology followed from POSTIT to NOMIT tends to enhance the belief that parameters ( i.e. length , shape , relevance , etc ... ) for an automatic heading procedure have to be studied and well defined , thus leading to a customized titling process .
Automatically titling documents is a complex task because of its subjectivity and titles must be informative, catchy and syntactically correct.
They propose to approach automatic titling by normalizing a verb phrase selected to be relevant into a noun phrase with morphological and semantic processing.
They show that the proposed normalizing process can produce informative and/or catchy titles but evaluations remain challenging due to its subjectivity.