Summarizing Dialogic Arguments from Social Media

Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form such a summary should take. Previous work on summarization has primarily focused on summarizing written texts, where the notion of an abstract of the text is well defined. We collect gold standard training data consisting of five human summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control and Abortion. We present several different computational models aimed at identifying segments of the dialogues whose content should be used for the summary, using linguistic features and Word2vec features with both SVMs and Bidirectional LSTMs. We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0.74 for gun control, 0.71 for gay marriage, and 0.67 for abortion.Comment: Proceedings of the 21th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2017

Automatic Structured Text Summarization with Concept Maps

Efficiently exploring a collection of text documents in order to answer a complex question is a challenge that many people face. As abundant information on almost any topic is electronically available nowadays, supporting tools are needed to ensure that people can profit from the information's availability rather than suffer from the information overload. Structured summaries can help in this situation: They can be used to provide a concise overview of the contents of a document collection, they can reveal interesting relationships and they can be used as a navigation structure to further explore the documents. A concept map, which is a graph representing concepts and their relationships, is a specific form of a structured summary that offers these benefits. However, despite its appealing properties, only a limited amount of research has studied how concept maps can be automatically created to summarize documents. Automating that task is challenging and requires a variety of text processing techniques including information extraction, coreference resolution and summarization. The goal of this thesis is to better understand these challenges and to develop computational models that can address them. As a first contribution, this thesis lays the necessary ground for comparable research on computational models for concept map--based summarization. We propose a precise definition of the task together with suitable evaluation protocols and carry out experimental comparisons of previously proposed methods. As a result, we point out limitations of existing methods and gaps that have to be closed to successfully create summary concept maps. Towards that end, we also release a new benchmark corpus for the task that has been created with a novel, scalable crowdsourcing strategy. Furthermore, we propose new techniques for several subtasks of creating summary concept maps. First, we introduce the usage of predicate-argument analysis for the extraction of concept and relation mentions, which greatly simplifies the development of extraction methods. Second, we demonstrate that a predicate-argument analysis tool can be ported from English to German with low effort, indicating that the extraction technique can also be applied to other languages. We further propose to group concept mentions using pairwise classifications and set partitioning, which significantly improves the quality of the created summary concept maps. We show similar improvements for a new supervised importance estimation model and an optimal subgraph selection procedure. By combining these techniques in a pipeline, we establish a new state-of-the-art for the summarization task. Additionally, we study the use of neural networks to model the summarization problem as a single end-to-end task. While such approaches are not yet competitive with pipeline-based approaches, we report several experiments that illustrate the challenges - mostly related to training data - that currently limit the performance of this technique. We conclude the thesis by presenting a prototype system that demonstrates the use of automatically generated summary concept maps in practice and by pointing out promising directions for future research on the topic of this thesis

Graph-based Patterns for Local Coherence Modeling

Coherence is an essential property of well-written texts. It distinguishes a multi-sentence text from a sequence of randomly strung sentences. The task of local coherence modeling is about the way that sentences in a text link up one another. Solving this task is beneficial for assessing the quality of texts. Moreover, a coherence model can be integrated into text generation systems such as text summarizers to produce coherent texts. In this dissertation, we present a graph-based approach to local coherence modeling that accounts for the connectivity structure among sentences in a text. Graphs give our model the capability to take into account relations between non-adjacent sentences as well as those between adjacent sentences. Besides, the connectivity style among nodes in graphs reflects the relationships among sentences in a text. We first employ the entity graph approach, proposed by Guinaudeau and Strube (2013), to represent a text via a graph. In the entity graph representation of a text, nodes encode sentences and edges depict the existence of a pair of coreferent mentions in sentences. We then devise graph-based features to capture the connectivity structure of nodes in a graph, and accordingly the connectivity structure of sentences in the corresponding text. We extract all subgraphs of entity graphs as features which encode the connectivity structure of graphs. Frequencies of subgraphs correlate with the perceived coherence of their corresponding texts. Therefore, we refer to these subgraphs as coherence patterns. In order to complete our approach to coherence modeling, we propose a new graph representation of texts, rather than the entity graph. Our approach employs lexico-semantic relations among words in sentences, instead of only entity coreference relations, to model relationships between sentences via a graph. This new lexical graph representation of text plus our method for mining coherence patterns make our coherence model. We evaluate our approach on the readability assessment task because a primary factor of readability is coherence. Coherent texts are easy to read and consequently demand less effort from their readers. Our extensive experiments on two separate readability assessment datasets show that frequencies of coherence patterns in texts correlate with the readability ratings assigned by human judges. By training a machine learning method on our coherence patterns, our model outperforms its counterparts on ranking texts with respect to their readability. As one of the ultimate goals of coherence models is to be used in text generation systems, we show how our coherence patterns can be integrated into a graph-based text summarizer to produce informative and coherent summaries. Our coherence patterns improve the performance of the summarization system based on both standard summarization metrics and human evaluations. An implementation of the approaches discussed in this dissertation is publicly available

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

Review of coreference resolution in English and Persian

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.Comment: 44 pages, 11 figures, 5 table

Entity-centric knowledge discovery for idiosyncratic domains

Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods