Automatic text summarization with Maximal Frequent Sequences

En las últimas dos décadas un aumento exponencial de la información electrónica ha provocado una gran necesidad de entender rápidamente grandes volúmenes de información. En este libro se desarrollan los métodos automáticos para producir un resumen. Un resumen es un texto corto que transmite la información más importante de un documento o de una colección de documentos. Los resúmenes utilizados en este libro son extractivos: una selección de las oraciones más importantes del texto. Otros retos consisten en generar resúmenes de manera independiente de lenguaje y dominio. Se describe la identificación de cuatro etapas para generación de resúmenes extractivos. La primera etapa es la selección de términos, en la que uno tiene que decidir qué unidades contarían como términos individuales. El proceso de estimación de la utilidad de los términos individuales se llama etapa de pesado de términos. El siguiente paso se denota como pesado de oraciones, donde todas las secuencias reciben alguna medida numérica de acuerdo con la utilidad de términos. Finalmente, el proceso de selección de las oraciones más importantes se llama selección de oraciones. Los diferentes métodos para generación de resúmenes extractivos pueden ser caracterizados como representan estas etapas. En este libro se describe la etapa de selección de términos, en la que la detección de descripciones multipalabra se realiza considerando Secuencias Frecuentes Maximales (sfms), las cuales adquieren un significado importante, mientras Secuencias Frecuentes (sf) no maximales, que son partes de otros sf, no deben de ser consideradas. En la motivación se consideró costo vs. beneficio: existen muchas sf no maximales, mientras que la probabilidad de adquirir un significado importante es baja. De todos modos, las sfms representan todas las sfs en el modo compacto: todas las sfs podrían ser obtenidas a partir de todas las sfms explotando cada sfm al conjunto de todas sus subsecuencias. Se presentan los nuevos métodos basados en grafos, algoritmos de agrupamiento y algoritmos genéticos, los cuales facilitan la tarea de generación de resúmenes de textos. Se ha experimentado diferentes combinaciones de las opciones de selección de términos, pesado de términos, pesado de oraciones y selección de oraciones para generar los resúmenes extractivos de textos independientes de lenguaje y dominio para una colección de noticias. Se ha analizado algunas opciones basadas en descripciones multipalabra considerándolas en los métodos de grafos, algoritmos de agrupamiento y algoritmos genéticos. Se han obtenido los resultados superiores al de estado de arte. Este libro está dirigido a los estudiantes y científicos del área de Lingüística Computacional, y también a quienes quieren saber sobre los recientes avances en las investigaciones de generación automática de resúmenes de textos.In the last two decades, an exponential increase in the available electronic information causes a big necessity to quickly understand large volumes of information. It raises the importance of the development of automatic methods for detecting the most relevant content of a document in order to produce a shorter text. Automatic Text Summarization (ats) is an active research area dedicated to generate abstractive and extractive summaries not only for a single document, but also for a collection of documents. Other necessity consists in finding method for ats in a language and domain independent way. In this book we consider extractive text summarization for single document task. We have identified that a typical extractive summarization method consists in four steps. First step is a term selection where one should decide what units will count as individual terms. The process of estimating the usefulness of the individual terms is called term weighting step. The next step denotes as sentence weighting where all the sentences receive some numerical measure according to the usefulness of its terms. Finally, the process of selecting the most relevant sentences calls sentence selection. Different extractive summarization methods can be characterized how they perform these steps. In this book, in the term selection step, we describe how to detect multiword descriptions considering Maximal Frequent Sequences (mfss), which bearing important meaning, while non-maximal frequent sequences (fss), those that are parts of another fs, should not be considered. Our additional motivation was cost vs. benefit considerations: there are too many non-maximal fss while their probability to bear important meaning is lower. In any case, mfss represent all fss in a compact way: all fss can be obtained from all mfss by bursting each mfs into a set of all its subsequences.New methods based on graph algorithms, genetic algorithms, and clustering algorithms which facilitate the text summarization task are presented. We have tested different combinations of term selection, term weighting, sentence weighting and sentence selection options for language-and domain-independent extractive single-document text summarization on a news report collection. We analyzed several options based on mfss, considering them with graph, genetic, and clustering algorithms. We obtained results superior to the existing state-ofthe- art methods. This book is addressed for students and scientists of the area of Computational Linguistics, and also who wants to know recent developments in the area of Automatic Text Generation of Summaries

Lexical cohesion based topic modeling for summarization

In this paper, we attack the problem of forming extracts for text summarization. Forming extracts involves selecting the most representative and significant sentences from the text. Our method takes advantage of the lexical cohesion structure in the text in order to evaluate significance of sentences. Lexical chains have been used in summarization research to analyze the lexical cohesion structure and represent topics in a text. Our algorithm represents topics by sets of co-located lexical chains to take advantage of more lexical cohesion clues. Our algorithm segments the text with respect to each topic and finds the most important topic segments. Our summarization algorithm has achieved better results, compared to some other lexical chain based algorithms. © 2008 Springer-Verlag Berlin Heidelberg

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

Automatic text summarisation using linguistic knowledge-based semantics

Text summarisation is reducing a text document to a short substitute summary. Since the commencement of the field, almost all summarisation research works implemented to this date involve identification and extraction of the most important document/cluster segments, called extraction. This typically involves scoring each document sentence according to a composite scoring function consisting of surface level and semantic features. Enabling machines to analyse text features and understand their meaning potentially requires both text semantic analysis and equipping computers with an external semantic knowledge. This thesis addresses extractive text summarisation by proposing a number of semantic and knowledge-based approaches. The work combines the high-quality semantic information in WordNet, the crowdsourced encyclopaedic knowledge in Wikipedia, and the manually crafted categorial variation in CatVar, to improve the summary quality. Such improvements are accomplished through sentence level morphological analysis and the incorporation of Wikipedia-based named-entity semantic relatedness while using heuristic algorithms. The study also investigates how sentence-level semantic analysis based on semantic role labelling (SRL), leveraged with a background world knowledge, influences sentence textual similarity and text summarisation. The proposed sentence similarity and summarisation methods were evaluated on standard publicly available datasets such as the Microsoft Research Paraphrase Corpus (MSRPC), TREC-9 Question Variants, and the Document Understanding Conference 2002, 2005, 2006 (DUC 2002, DUC 2005, DUC 2006) Corpora. The project also uses Recall-Oriented Understudy for Gisting Evaluation (ROUGE) for the quantitative assessment of the proposed summarisers’ performances. Results of our systems showed their effectiveness as compared to related state-of-the-art summarisation methods and baselines. Of the proposed summarisers, the SRL Wikipedia-based system demonstrated the best performance

Extracting fine-grained economic events from business news

Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50{%} are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap

Semantic annotation and summarization of biomedical text

Advancements in the biomedical community are largely documented and published in text format in scientific forums such as conference papers and journals. To address the scalability of utilizing the large volume of text-based information generated by continuing advances in the biomedical field, two complementary areas are studied. The first area is Semantic Annotation, which is a method for providing machineunderstandable information based on domain-specific resources. A novel semantic annotator, CONANN, is implemented for online matching of concepts defined by a biomedical metathesaurus. CONANN uses a multi-level filter based on both information retrieval and shallow natural language processing techniques. CONANN is evaluated against a state-of-the-art biomedical annotator using the performance measures of time (e.g. number of milliseconds per noun phrase) and precision/recall of the resulting concept matches. CONANN shows that annotation can be performed online, rather than offline, without a significant loss of precision and recall as compared to current offline systems. The second area of study is Text Summarization which is used as a way to perform data reduction of clinical trial texts while still describing the main themes of a biomedical document. The text summarization work is unique in that it focuses exclusively on summarizing biomedical full-text sources as opposed to abstracts, and also exclusively uses domain-specific concepts, rather than terms, to identify important information within a biomedical text. Two novel text summarization algorithms are implemented: one using a concept chaining method based on existing work in lexical chaining (BioChain), and the other using concept distribution to match important sentences between a source text and a generated summary (FreqDist). The BioChain and FreqDist summarizers are evaluated using the publicly-available ROUGE summary evaluation tool. ROUGE compares n-gram co-occurrences between a system summary and one or more model summaries. The text summarization evaluation shows that the two approaches outperform nearly all of the existing term-based approaches.Ph.D., Information Science and Technology -- Drexel University, 200

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

Generating Faithful and Complete Hospital-Course Summaries from the Electronic Health Record

The rapid adoption of Electronic Health Records (EHRs)--electronic versions of a patient's medical history--has been instrumental in streamlining administrative tasks, increasing transparency, and enabling continuity of care across providers. An unintended consequence of the increased documentation burden, however, has been reduced face-time with patients and, concomitantly, a dramatic rise in clinician burnout. Time spent maintaining and making sense of a patient's electronic record is a leading cause of burnout. In this thesis, we pinpoint a particularly time-intensive, yet critical, documentation task: generating a summary of a patient's hospital admissions, and propose and evaluate automated solutions. In particular, we focus on faithfulness, i.e., accurately representing the patient record, and completeness, i.e., representing the full context, as the sine qua non for safe deployment of a hospital-course summarization tool in a clinical setting. The bulk of this thesis is broken up into four chapters: §2 Creating and Analyzing the Data, §3 Improving the Faithfulness of Summaries, §4 Measuring the Faithfulness of Summaries, and, finally, §5 Generating Grounded, Complete Summaries with LLMs. Each chapter links back to the core themes of faithfulness and completeness, while the chapters are linked to each other in that the findings from each chapter shape the direction of subsequent chapters. Given the documentation authored throughout a patient's hospitalization, hospital-course summarization requires generating a lengthy paragraph that tells the story of the patient admission. In § 2, we construct a dataset based on 109,000 hospitalizations (2M source notes) and perform exploratory analyses to motivate future work on modeling and evaluation [NAACL 2021]. The presence of highly abstractive, entity dense references, coupled with the high stakes nature of text generation in a clinical setting, motivates us to focus on faithfulness and adequate coverage of salient medical entities. In § 3, we address faithfulness from a modeling perspective by revising noisy references [EMNLP 2022] and, to reduce the reliance on references, directly calibrating model outputs to metrics [ACL 2023]. These works relied heavily on automatic metrics as human annotations were limited. To fill this gap, in §4, we conduct a fine-grained expert annotation of system errors in order to meta-evaluate existing metrics and better understand task-specific issues of domain adaptation and source-summary alignments. We find that automatically generated summaries can exhibit many errors, including incorrect claims and critical omissions, despite being highly extractive. These errors are missed by existing metrics. To learn a metric which is less correlated to extractiveness (copy-and-paste), we derive noisy faithfulness labels from an ensemble of existing metrics and train a faithfulness classifier on these pseudo labels [MLHC 2023]. Finally, in § 5, we demonstrate that fine-tuned LLMs (Mistral and Zephyr) are highly prone to entity hallucinations and cover fewer salient entities. We improve both coverage and faithfulness by performing sentence-level entity planning based on a set of pre-computed salient entities from the source text, which extends our work on entity-guided news summarization ([ACL, 2023] and [EMNLP, 2023])