Disinformation is an important problem facing society nowadays. Given the rapid and easy access to information, news stories quickly go viral, the vast majority of which are misleading and with no prospect of verification. Specifically, the headline of a correctly designed news item must correspond to a summary of the main information of that news item and it should be neutral. However, many headlines circulating on the Internet use false or distorted information, seeking to confuse or mislead the reader. Misleading headlines indicate a dissonance between the headline and the content of the news story. From a computational perspective, this problem is being tackled as a Stance Detection problem between the headline and the body text of the news item. This paper contributes to the fight against the spread of misleading information by presenting a generic and flexible multi-level hierarchical classification. The approach is based on two stages that enable the detection of the stance between the news headline and the body text. The proposed architecture, called HeadlineStanceChecker+ uses the headline and only the essential information of the news item (not the full body text) as inputs. To extract this essential information, different summarization approaches (extractive and abstractive) are analyzed in order to determine the most relevant information for the task. The experimentation has been carried out using the Fake News Challenge (FNC-1) dataset. A 94.49% accuracy was obtained using extractive summaries, which were more helpful than abstractive ones. HeadlineStanceChecker+ improves the accuracy results of existing state-of-the-art systems. In conclusion, using automatic extractive summaries together with the two-stage generic architecture is an effective solution to the problem.

Headline generation is a task where the most important information of a news article is condensed and embodied into a single short sentence. This task is normally addressed by summarization techniques, ideally combining extractive and abstractive methods together with sentence compression or fusion techniques. Although Natural Language Generation (NLG) techniques have not been directly exploited for headline generation, they may provide better mechanisms than summarization techniques to paraphrase the information of a text. Therefore, this paper analyzes and evaluates the effectiveness of NLG techniques for generating headlines. In NLG, both content selection and surface realization are equally important—there is no point in generating text without knowing the topic. Considering this premise, we therefore take HanaNLG—a hybrid surface realization approach—as a basis, and we analyze the effect in the generated text when different content selection strategies are integrated at macroplanning stage. The experiments conducted show that, despite not using any sophisticated summarization method, the proposed approach provided the following benefits: i) it generated a coherent, linguistically structured headline; ii) it obtained results on standard datasets (i.e., DUC 2003 and DUC 2004) that were comparable to several competitive systems, in terms of the content of the generated headline; and, iii) the headlines generated by the whole approach (PLM-HanaNLG) were preferred by human assessors compared to those generated by the best performing system in DUC 2003.

The headline of a news article is designed to succinctly summarize its content, providing the reader with a clear understanding of the news item. Unfortunately, in the post-truth era, headlines are more focused on attracting the reader’s attention for ideological or commercial reasons, thus leading to mis- or disinformation through false or distorted headlines. One way of combating this, although a challenging task, is by determining the relation between the headline and the body text to establish the stance. Hence, to contribute to the detection of mis- and disinformation, this paper proposes an approach (HeadlineStanceChecker) that determines the stance of a headline with respect to the body text to which it is associated. The novelty rests on the use of a two-stage classification architecture that uses summarization techniques to shape the input for both classifiers instead of directly passing the full news body text, thereby reducing the amount of information to be processed while keeping important information. Specifically, summarization is done through Positional Language Models leveraging on semantic resources to identify salient information in the body text that is then compared to its corresponding headline. The results obtained show that our approach achieves 94.31% accuracy for the overall classification and the best FNC-1 relative score compared with the state of the art. It is especially remarkable that the system, which uses only the relevant information provided by the automatic summaries instead of the whole text, is able to classify the different stance categories with very competitive results, especially in the discuss stance between the headline and the news body text. It can be concluded that using automatic extractive summaries as input of our approach together with the two-stage architecture is an appropriate solution to the problem.

In this paper we present the approach and results of our participation in the 2015 MultiLing Single-document Summarization task.Our approach is based on the Principal Component Analysis (PCA) technique enhanced with lexical-semantic knowledge.For testing our approach, different configurations were set up, thus generating different types of summaries (i.e., generic and topic-focused), as well as testing some language-specific resources on top of the language-independent basic PCA approach, submitting a total of 6 runs for each selected language (English, German, and Spanish).Our participation in MultiLing has been very positive, ranking at intermediate positions when compared to the other participant systems, showing that PCA is a good technique for generating language-independent summaries, but the addition of lexical-semantic knowledge may heavily depend on the size and quality of the resources available for each language.