A reinforcement learning formulation to the complex question answering problem

International audienceWe use extractive multi-document summarization techniques to perform complex question answering and formulate it as a reinforcement learning problem. Given a set of complex questions, a list of relevant documents per question, and the corresponding human generated summaries (i.e. answers to the questions) as training data, the reinforcement learning module iteratively learns a number of feature weights in order to facilitate the automatic generation of summaries i.e. answers to previously unseen complex questions. A reward function is used to measure the similarities between the candidate (machine generated) summary sentences and the abstract summaries. In the training stage, the learner iteratively selects the important document sentences to be included in the candidate summary, analyzes the reward function and updates the related feature weights accordingly. The final weights are used to generate summaries as answers to unseen complex questions in the testing stage. Evaluation results show the effectiveness of our system. We also incorporate user interaction into the reinforcement learner to guide the candidate summary sentence selection process. Experiments reveal the positive impact of the user interaction component on the reinforcement learning framework

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)

Arabic multi-document text summarisation

Multi-document summarisation is the process of producing a single summary of a collection of related documents. Much of the current work on multi-document text summarisation is concerned with the English language; relevant resources are numerous and readily available. These resources include human generated (gold-standard) and automatic summaries. Arabic multi-document summarisation is still in its infancy. One of the obstacles to progress is the limited availability of Arabic resources to support this research. When we started our research there were no publicly available Arabic multi-document gold-standard summaries, which are needed to automatically evaluate system generated summaries. The Document Understanding Conference (DUC) and Text Analysis Conference (TAC) at that time provided resources such as gold-standard extractive and abstractive summaries (both human and system generated) that were only available in English. Our aim was to push forward the state-of-the-art in Arabic multi-document summarisation. This required advancements in at least two areas. The first area was the creation of Arabic test collections. The second area was concerned with the actual summarisation process to find methods that improve the quality of Arabic summaries. To address both points we created single and multi-document Arabic test collections both automatically and manually using a commonly used English dataset and by having human participants. We developed extractive language dependent and language independent single and multi-document summarisers, both for Arabic and English. In our work we provided state-of-the-art approaches for Arabic multi-document summarisation. We succeeded in including Arabic in one of the leading summarisation conferences the Text Analysis Conference (TAC). Researchers on Arabic multi-document summarisation now have resources and tools that can be used to advance the research in this field

Older versions of the ROUGEeval summarization evaluation system were easier to fool

We show some limitations of the ROUGE evaluation method for automatic summarization. We present a method for automatic summarization based on a Markov model of the source text. By a simple greedy word selection strategy, summaries with high ROUGE-scores are generated. These summaries would however not be considered good by human readers. The method can be adapted to trick different settings of the ROUGEeval package. Key words: automatic summarization, automatic evaluation, Markov models

Indexation aléatoire et similarité inter-phrases appliquées au résumé automatique

With the growing mass of textual data on the Web, automatic summarization of topic-oriented collections of documents has become an important research field of Natural Language Processing. The experiments described in this thesis were framed within this context. Evaluating the semantic similarity between sentences is central to our work and we based our approach on distributional similarity and vector representation of terms, with Wikipedia as a reference corpus. We proposed several similarity measures which were evaluated and compared on different data sets: the SemEval 2014 challenge corpus for the English language and own built datasets for French. The good performance showed by our measures led us to use them in a multi-document summary task, which implements a pagerank-type algorithm. The system was evaluated on the DUC 2007 datasets for English and RPM2 corpus for French. This simple approach, based on a resource readily available in many languages, proved efficient, robust and the encouraging outcomes open up real prospects of improvement.Face à la masse grandissante des données textuelles présentes sur le Web, le résumé automatique d'une collection de documents traitant d'un sujet particulier est devenu un champ de recherche important du Traitement Automatique des Langues. Les expérimentations décrites dans cette thèse s'inscrivent dans cette perspective. L'évaluation de la similarité sémantique entre phrases est l'élément central des travaux réalisés. Notre approche repose sur la similarité distributionnelle et une vectorisation des termes qui utilise l'encyclopédie Wikipédia comme corpus de référence. Sur la base de cette représentation, nous avons proposé, évalué et comparé plusieurs mesures de similarité textuelle ; les données de tests utilisées sont celles du défi SemEval 2014 pour la langue anglaise et des ressources que nous avons construites pour la langue française. Les bonnes performances des mesures proposées nous ont amenés à les utiliser dans une tâche de résumé multi-documents, qui met en oeuvre un algorithme de type PageRank. Le système a été évalué sur les données de DUC 2007 pour l'anglais et le corpus RPM2 pour le français. Les résultats obtenus par cette approche simple, robuste et basée sur une ressource aisément disponible dans de nombreuses langues, se sont avérés très encourageant

Complex question answering : minimizing the gaps and beyond

xi, 192 leaves : ill. ; 29 cmCurrent Question Answering (QA) systems have been significantly advanced in demonstrating finer abilities to answer simple factoid and list questions. Such questions are easier to process as they require small snippets of texts as the answers. However, there is a category of questions that represents a more complex information need, which cannot be satisfied easily by simply extracting a single entity or a single sentence. For example, the question: “How was Japan affected by the earthquake?” suggests that the inquirer is looking for information in the context of a wider perspective. We call these “complex questions” and focus on the task of answering them with the intention to minimize the existing gaps in the literature. The major limitation of the available search and QA systems is that they lack a way of measuring whether a user is satisfied with the information provided. This was our motivation to propose a reinforcement learning formulation to the complex question answering problem. Next, we presented an integer linear programming formulation where sentence compression models were applied for the query-focused multi-document summarization task in order to investigate if sentence compression improves the overall performance. Both compression and summarization were considered as global optimization problems. We also investigated the impact of syntactic and semantic information in a graph-based random walk method for answering complex questions. Decomposing a complex question into a series of simple questions and then reusing the techniques developed for answering simple questions is an effective means of answering complex questions. We proposed a supervised approach for automatically learning good decompositions of complex questions in this work. A complex question often asks about a topic of user’s interest. Therefore, the problem of complex question decomposition closely relates to the problem of topic to question generation. We addressed this challenge and proposed a topic to question generation approach to enhance the scope of our problem domain

Principled Approaches to Automatic Text Summarization

Automatic text summarization is a particularly challenging Natural Language Processing (NLP) task involving natural language understanding, content selection and natural language generation. In this thesis, we concentrate on the content selection aspect, the inherent problem of summarization which is controlled by the notion of information Importance. We present a simple and intuitive formulation of the summarization task as two components: a summary scoring function θ measuring how good a text is as a summary of the given sources, and an optimization technique O extracting a summary with a high score according to θ. This perspective offers interesting insights over previous summarization efforts and allows us to pinpoint promising research directions. In particular, we realize that previous works heavily constrained the summary scoring function in order to solve convenient optimization problems (e.g., Integer Linear Programming). We question this assumption and demonstrate that General Purpose Optimization (GPO) techniques like genetic algorithms are practical. These GPOs do not require mathematical properties from the objective function and, thus, the summary scoring function can be relieved from its previously imposed constraints. Additionally, the summary scoring function can be evaluated on its own based on its ability to correlate with humans. This offers a principled way of examining the inner workings of summarization systems and complements the traditional evaluations of the extracted summaries. In fact, evaluation metrics are also summary scoring functions which should correlate well with humans. Thus, the two main challenges of summarization, the evaluation and the development of summarizers, are unified within the same setup: discovering strong summary scoring functions. Hence, we investigated ways of uncovering such functions. First, we conducted an empirical study of learning the summary scoring function from data. The results show that an unconstrained summary scoring function is better able to correlate with humans. Furthermore, an unconstrained summary scoring function optimized approximately with GPO extracts better summaries than a constrained summary scoring function optimized exactly with, e.g., ILP. Along the way, we proposed techniques to leverage the small and biased human judgment datasets. Additionally, we released a new evaluation metric explicitly trained to maximize its correlation with humans. Second, we developed a theoretical formulation of the notion of Importance. In a framework rooted in information theory, we defined the quantities: Redundancy, Relevance and Informativeness. Importance arises as the notion unifying these concepts. More generally, Importance is the measure that guides which choices to make when information must be discarded. Finally, evaluation remains an open-problem with a massive impact on summarization progress. Thus, we conducted experiments on available human judgment datasets commonly used to compare evaluation metrics. We discovered that these datasets do not cover the high-quality range in which summarization systems and evaluation metrics operate. This motivates efforts to collect human judgments for high-scoring summaries as this would be necessary to settle the debate over which metric to use. This would also be greatly beneficial for improving summarization systems and metrics alike

Supervised extractive summarisation of news events

This thesis investigates whether the summarisation of news-worthy events can be improved by using evidence about entities (i.e.\ people, places, and organisations) involved in the events. More effective event summaries, that better assist people with their news-based information access requirements, can help to reduce information overload in today's 24-hour news culture. Summaries are based on sentences extracted verbatim from news articles about the events. Within a supervised machine learning framework, we propose a series of entity-focused event summarisation features. Computed over multiple news articles discussing a given event, such entity-focused evidence estimates: the importance of entities within events; the significance of interactions between entities within events; and the topical relevance of entities to events. The statement of this research work is that augmenting supervised summarisation models, which are trained on discriminative multi-document newswire summarisation features, with evidence about the named entities involved in the events, by integrating entity-focused event summarisation features, we will obtain more effective summaries of news-worthy events. The proposed entity-focused event summarisation features are thoroughly evaluated over two multi-document newswire summarisation scenarios. The first scenario is used to evaluate the retrospective event summarisation task, where the goal is to summarise an event to-date, based on a static set of news articles discussing the event. The second scenario is used to evaluate the temporal event summarisation task, where the goal is to summarise the changes in an ongoing event, based on a time-stamped stream of news articles discussing the event. The contributions of this thesis are two-fold. First, this thesis investigates the utility of entity-focused event evidence for identifying important and salient event summary sentences, and as a means to perform anti-redundancy filtering to control the volume of content emitted as a summary of an evolving event. Second, this thesis also investigates the validity of automatic summarisation evaluation metrics, the effectiveness of standard summarisation baselines, and the effective training of supervised machine learned summarisation models