Semantics-driven Abstractive Document Summarization

The evolution of the Web over the last three decades has led to a deluge of scientific and news articles on the Internet. Harnessing these publications in different fields of study is critical to effective end user information consumption. Similarly, in the domain of healthcare, one of the key challenges with the adoption of Electronic Health Records (EHRs) for clinical practice has been the tremendous amount of clinical notes generated that can be summarized without which clinical decision making and communication will be inefficient and costly. In spite of the rapid advances in information retrieval and deep learning techniques towards abstractive document summarization, the results of these efforts continue to resemble extractive summaries, achieving promising results predominantly on lexical metrics but performing poorly on semantic metrics. Thus, abstractive summarization that is driven by intrinsic and extrinsic semantics of documents is not adequately explored. Resources that can be used for generating semantics-driven abstractive summaries include: • Abstracts of multiple scientific articles published in a given technical field of study to generate an abstractive summary for topically-related abstracts within the field, thus reducing the load of having to read semantically duplicate abstracts on a given topic. • Citation contexts from different authoritative papers citing a reference paper can be used to generate utility-oriented abstractive summary for a scientific article. • Biomedical articles and the named entities characterizing the biomedical articles along with background knowledge bases to generate entity and fact-aware abstractive summaries. • Clinical notes of patients and clinical knowledge bases for abstractive clinical text summarization using knowledge-driven multi-objective optimization. In this dissertation, we develop semantics-driven abstractive models based on intra- document and inter-document semantic analyses along with facts of named entities retrieved from domain-specific knowledge bases to produce summaries. Concretely, we propose a sequence of frameworks leveraging semantics at various granularity (e.g., word, sentence, document, topic, citations, and named entities) levels, by utilizing external resources. The proposed frameworks have been applied to a range of tasks including 1. Abstractive summarization of topic-centric multi-document scientific articles and news articles. 2. Abstractive summarization of scientific articles using crowd-sourced citation contexts. 3. Abstractive summarization of biomedical articles clustered based on entity-relatedness. 4. Abstractive summarization of clinical notes of patients with heart failure and Chest X-Rays recordings. The proposed approaches achieve impressive performance in terms of preserving semantics in abstractive summarization while paraphrasing. For summarization of topic-centric multiple scientific/news articles, we propose a three-stage approach where abstracts of scientific articles or news articles are clustered based on their topical similarity determined from topics generated using Latent Dirichlet Allocation (LDA), followed by extractive phase and abstractive phase. Then, in the next stage, we focus on abstractive summarization of biomedical literature where we leverage named entities in biomedical articles to 1) cluster related articles; and 2) leverage the named entities towards guiding abstractive summarization. Finally, in the last stage, we turn to external resources such as citation contexts pointing to a scientific article to generate a comprehensive and utility-centric abstractive summary of a scientific article, domain-specific knowledge bases to fill gaps in information about entities in a biomedical article to summarize and clinical notes to guide abstractive summarization of clinical text. Thus, the bottom-up progression of exploring semantics towards abstractive summarization in this dissertation starts with (i) Semantic Analysis of Latent Topics; builds on (ii) Internal and External Knowledge-I (gleaned from abstracts and Citation Contexts); and extends it to make it comprehensive using (iii) Internal and External Knowledge-II (Named Entities and Knowledge Bases)

Descartes: Generating Short Descriptions of Wikipedia Articles

Wikipedia is one of the richest knowledge sources on the Web today. In order to facilitate navigating, searching, and maintaining its content, Wikipedia's guidelines state that all articles should be annotated with a so-called short description indicating the article's topic (e.g., the short description of beer is "Alcoholic drink made from fermented cereal grains"). Nonetheless, a large fraction of articles (ranging from 10.2% in Dutch to 99.7% in Kazakh) have no short description yet, with detrimental effects for millions of Wikipedia users. Motivated by this problem, we introduce the novel task of automatically generating short descriptions for Wikipedia articles and propose Descartes, a multilingual model for tackling it. Descartes integrates three sources of information to generate an article description in a target language: the text of the article in all its language versions, the already-existing descriptions (if any) of the article in other languages, and semantic type information obtained from a knowledge graph. We evaluate a Descartes model trained for handling 25 languages simultaneously, showing that it beats baselines (including a strong translation-based baseline) and performs on par with monolingual models tailored for specific languages. A human evaluation on three languages further shows that the quality of Descartes's descriptions is largely indistinguishable from that of human-written descriptions; e.g., 91.3% of our English descriptions (vs. 92.1% of human-written descriptions) pass the bar for inclusion in Wikipedia, suggesting that Descartes is ready for production, with the potential to support human editors in filling a major gap in today's Wikipedia across languages

Evaluation of Automatic Text Summarization Using Synthetic Facts

Automatic text summarization has achieved remarkable success with the development of deep neural networks and the availability of standardized benchmark datasets. It can generate fluent, human-like summaries. However, the unreliability of the existing evaluation metrics hinders its practical usage and slows down its progress. To address this issue, we propose an automatic reference-less text summarization evaluation system with dynamically generated synthetic facts. We hypothesize that if a system guarantees a summary that has all the facts that are 100% known in the synthetic document, it can provide natural interpretability and high feasibility in measuring factual consistency and comprehensiveness. To our knowledge, our system is the first system that measures the overarching quality of the text summarization models with factual consistency, comprehensiveness, and compression rate. We validate our system by comparing its correlation with human judgment with existing N-gram overlap-based metrics such as ROUGE and BLEU and a BERT-based evaluation metric, BERTScore. Our system\u27s experimental evaluation of PEGASUS, BART, and T5 outperforms the current evaluation metrics in measuring factual consistency with a noticeable margin and demonstrates its statistical significance in measuring comprehensiveness and overall summary quality

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Text-image synergy for multimodal retrieval and annotation

Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text und Bild sind die beiden häufigsten Arten von Inhalten im Internet. Während es für Menschen einfach ist, gerade aus dem Zusammenspiel von Text- und Bildinhalten Informationen zu erfassen, stellt diese kombinierte Darstellung von Inhalten Softwaresysteme vor große Herausforderungen. In dieser Dissertation werden Probleme studiert, für deren Lösung das Verständnis des Zusammenspiels von Text- und Bildinhalten wesentlich ist. Es werden Methoden und Vorschläge präsentiert und empirisch bewertet, die semantische Verbindungen zwischen Text und Bild in multimodalen Daten herstellen. Wir stellen in dieser Dissertation vier miteinander verbundene Text- und Bildprobleme vor: • Bildersuche. Ob Bilder anhand von textbasierten Suchanfragen gefunden werden, hängt stark davon ab, ob der Text in der Nähe des Bildes mit dem der Anfrage übereinstimmt. Bilder ohne textuellen Kontext, oder sogar mit thematisch passendem Kontext, aber ohne direkte Übereinstimmungen der vorhandenen Schlagworte zur Suchanfrage, können häufig nicht gefunden werden. Zur Abhilfe schlagen wir vor, drei Arten von Informationen in Kombination zu nutzen: visuelle Informationen (in Form von automatisch generierten Bildbeschreibungen), textuelle Informationen (Stichworte aus vorangegangenen Suchanfragen), und Alltagswissen. • Verbesserte Bildbeschreibungen. Bei der Objekterkennung durch Computer Vision kommt es des Öfteren zu Fehldetektionen und Inkohärenzen. Die korrekte Identifikation von Bildinhalten ist jedoch eine wichtige Voraussetzung für die Suche nach Bildern mittels textueller Suchanfragen. Um die Fehleranfälligkeit bei der Objekterkennung zu minimieren, schlagen wir vor Alltagswissen einzubeziehen. Durch zusätzliche Bild-Annotationen, welche sich durch den gesunden Menschenverstand als thematisch passend erweisen, können viele fehlerhafte und zusammenhanglose Erkennungen vermieden werden. • Bild-Text Platzierung. Auf Internetseiten mit Text- und Bildinhalten (wie Nachrichtenseiten, Blogbeiträge, Artikel in sozialen Medien) werden Bilder in der Regel an semantisch sinnvollen Positionen im Textfluss platziert. Wir nutzen dies um ein Framework vorzuschlagen, in dem relevante Bilder ausgesucht werden und mit den passenden Abschnitten eines Textes assoziiert werden. • Bildunterschriften. Bilder, die als Teil von multimodalen Inhalten zur Verbesserung der Lesbarkeit von Texten dienen, haben typischerweise Bildunterschriften, die zum Kontext des umgebenden Texts passen. Wir schlagen vor, den Kontext beim automatischen Generieren von Bildunterschriften ebenfalls einzubeziehen. Üblicherweise werden hierfür die Bilder allein analysiert. Wir stellen die kontextbezogene Bildunterschriftengenerierung vor. Unsere vielversprechenden Beobachtungen und Ergebnisse eröffnen interessante Möglichkeiten für weitergehende Forschung zur computergestützten Erfassung des Zusammenspiels von Text- und Bildinhalten

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

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