The challenging task of summary evaluation: an overview

Evaluation is crucial in the research and development of automatic summarization applications, in order to determine the appropriateness of a summary based on different criteria, such as the content it contains, and the way it is presented. To perform an adequate evaluation is of great relevance to ensure that automatic summaries can be useful for the context and/or application they are generated for. To this end, researchers must be aware of the evaluation metrics, approaches, and datasets that are available, in order to decide which of them would be the most suitable to use, or to be able to propose new ones, overcoming the possible limitations that existing methods may present. In this article, a critical and historical analysis of evaluation metrics, methods, and datasets for automatic summarization systems is presented, where the strengths and weaknesses of evaluation efforts are discussed and the major challenges to solve are identified. Therefore, a clear up-to-date overview of the evolution and progress of summarization evaluation is provided, giving the reader useful insights into the past, present and latest trends in the automatic evaluation of summaries.This research is partially funded by the European Commission under the Seventh (FP7 - 2007- 2013) Framework Programme for Research and Technological Development through the SAM (FP7-611312) project; by the Spanish Government through the projects VoxPopuli (TIN2013-47090-C3-1-P) and Vemodalen (TIN2015-71785-R), the Generalitat Valenciana through project DIIM2.0 (PROMETEOII/2014/001), and the Universidad Nacional de Educación a Distancia through the project “Modelado y síntesis automática de opiniones de usuario en redes sociales” (2014-001-UNED-PROY)

Unsupervised Induction of Frame-Based Linguistic Forms

This thesis studies the use of bulk, structured, linguistic annotations in order to perform unsupervised induction of meaning for three kinds of linguistic forms: words, sentences, and documents. The primary linguistic annotation I consider throughout this thesis are frames, which encode core linguistic, background or societal knowledge necessary to understand abstract concepts and real-world situations. I begin with an overview of linguistically-based structured meaning representation; I then analyze available large-scale natural language processing (NLP) and linguistic resources and corpora for their abilities to accommodate bulk, automatically-obtained frame annotations. I then proceed to induce meanings of the different forms, progressing from the word level, to the sentence level, and finally to the document level. I first show how to use these bulk annotations in order to better encode linguistic- and cognitive science backed semantic expectations within word forms. I then demonstrate a straightforward approach for learning large lexicalized and refined syntactic fragments, which encode and memoize commonly used phrases and linguistic constructions. Next, I consider two unsupervised models for document and discourse understanding; one is a purely generative approach that naturally accommodates layer annotations and is the first to capture and unify a complete frame hierarchy. The other conditions on limited amounts of external annotations, imputing missing values when necessary, and can more readily scale to large corpora. These discourse models help improve document understanding and type-level understanding

Automated Semantic Analysis, Legal Assessment, and Summarization of Standard Form Contracts

Consumers are confronted with standard form contracts on a daily basis, for example, when shopping online, registering for online platforms, or opening bank accounts. With expected revenue of more than 343 billion Euro in 2020, e-commerce is an ever more important branch of the European economy. Accepting standard form contracts often is a prerequisite to access products or services, and consumers frequently do so without reading, let alone understanding, them. Consumer protection organizations can advise and represent consumers in such situations of power imbalance. However, with increasing demand, limited budgets, and ever more complex regulations, they struggle to provide the necessary support. This thesis investigates techniques for the automated semantic analysis, legal assessment, and summarization of standard form contracts in German and English, which can be used to support consumers and those who protect them. We focus on Terms and Conditions from the fast growing market of European e-commerce, but also show that the developed techniques can in parts be applied to other types of standard form contracts. We elicited requirements from consumers and consumer advocates to understand their needs, identified the most relevant clause topics, and analyzed the processes in consumer protection organizations concerning the handling of standard form contracts. Based on these insights, a pipeline for the automated semantic analysis, legal assessment, and summarization of standard form contracts was developed. The components of this pipeline can automatically identify and extract standard form contracts from the internet and hierarchically structure them into their individual clauses. Clause topics can be automatically identified, and relevant information can be extracted. Clauses can then be legally assessed, either using a knowledge-base we constructed or through binary classification by a transformer model. This information is then used to create summaries that are tailored to the needs of the different user groups. For each step of the pipeline, different approaches were developed and compared, from classical rule-based systems to deep learning techniques. Each approach was evaluated on German and English corpora containing more than 10,000 clauses, which were annotated as part of this thesis. The developed pipeline was prototypically implemented as part of a web-based tool to support consumer advocates in analyzing and assessing standard form contracts. The implementation was evaluated with experts from two German consumer protection organizations with questionnaires and task-based evaluations. The results of the evaluation show that our system can identify over 50 different types of clauses, which cover more than 90% of the clauses typically occurring in Terms and Conditions from online shops, with an accuracy of 0.80 to 0.84. The system can also automatically extract 21 relevant data points from these clauses with a precision of 0.91 and a recall of 0.86. On a corpus of more than 200 German clauses, the system was also able to assess the legality of clauses with an accuracy of 0.90. The expert evaluation has shown that the system is indeed able to support consumer advocates in their daily work by reducing the time they need to analyze and assess clauses in standard form contracts

Cognitive structures of content for controlled summarization

In the current information age, where over 1 Petabyte of data is created every day on the web, demand continues to rise for effective technological tools to aid end-users in consuming information in a timely way. Automatic summarization is the task of consuming a text document –or collection of documents-- and presenting the user with a shorter text, the \textit{summary}, that retains the gist of the information consumed. In general, a good summary should present content bits that are relevant –be informative--, non-redundant -be non-repetitive--, organized in a sensical way –be coherent--, and read as a unified thematic whole –be cohesive. The particular information needs of each user prompted many variations of the summarization task. Among them, extractive summarization consists of extracting spans of text -usually sentences- from the input document(s), concatenating them, and presenting them as the final summary. Traditionally, extractive systems focus their attention on presenting highly informative content, regardless of whether content bits are repeated or presented in an incoherent, non-cohesive manner. How to balance these properties remains an understudied problem, even though the understanding of the trade-offs between them could enable a system to produce text with relevant content that is also more readable to humans. This thesis argues that extractive summaries can be presented in a non-redundant, cohesive way, and still be informative. We investigate the interaction between these summary properties and develop models that balance their trade-off during document understanding and during summary production. At the core of these models, an algorithm --inspired by psycholinguistic models of memory-- simulates how humans keep track of relevant content in short-term memory, and how cohesion and non-redundancy constraints are applied among content bits in memory. The results are encouraging. When modeling trade-off during document understanding in an unsupervised scenario, we find that our models are able to detect relevant content, reduce redundancy, and significantly improve cohesion in summaries, especially when the input document exhibits high redundancy. Furthermore, we show that this balance can be controlled through specific, interpretable hyper-parameters. In a similar reinforcement learning scenario, we find that informativeness and cohesion can influence each other positively. Finally, when modeling trade-off during summary extraction, our models are able to better enforce cohesive ties between semantically similar text spans in neighboring sentences. Our approach produces summaries that are perceived by humans as more cohesive and as informative as summaries only built for informativeness. Catering to the need to process extremely long and redundant input, we design this system to be capable of consuming sequences of text of arbitrary length and test it on scenarios with single, long documents, and multi-documents

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains