Summarization of emergency news articles driven by relevance feedback

Many articles on the same news are daily published by online newspapers and by various social media. To ease news article exploration sentence-based summarization algorithms aim at automatically generating for each news a summary consisting of the most salient sentences in the original articles. However, since sentence selection is error-prone, the automatically generated summaries are still subject to manual validation by domain experts. If the validation step not only focuses on pruning less relevant content but also on enriching summaries with missing yet relevant sentences this activity may become extremely time consuming. The paper focuses on summarizing news articles by means of an itemset-based technique. To tune summarizer performance a relevance feedback given on sentences is exploited to drive the generation of a new, more targeted summary. The feedback indicates the pertinence of the sentences that are already in the summary. Among the words or the word combinations selected by the summarization model, those occurring in sentences with high feedback score represent concepts that may be deemed as particularly relevant. Therefore, they are exploited to drive the new sentence selection process. The proposed approach was tested on collections of newsvarticles reporting emergency situations. The results show the effectiveness of the proposed approach

Document analysis by means of data mining techniques

The huge amount of textual data produced everyday by scientists, journalists and Web users, allows investigating many different aspects of information stored in the published documents. Data mining and information retrieval techniques are exploited to manage and extract information from huge amount of unstructured textual data. Text mining also known as text data mining is the processing of extracting high quality information (focusing relevance, novelty and interestingness) from text by identifying patterns etc. Text mining typically involves the process of structuring input text by means of parsing and other linguistic features or sometimes by removing extra data and then finding patterns from structured data. Patterns are then evaluated at last and interpretation of output is performed to accomplish the desired task. Recently, text mining has got attention in several fields such as in security (involves analysis of Internet news), for commercial (for search and indexing purposes) and in academic departments (such as answering query). Beyond searching the documents consisting the words given in a user query, text mining may provide direct answer to user by semantic web for content based (content meaning and its context). It can also act as intelligence analyst and can also be used in some email spam filters for filtering out unwanted material. Text mining usually includes tasks such as clustering, categorization, sentiment analysis, entity recognition, entity relation modeling and document summarization. In particular, summarization approaches are suitable for identifying relevant sentences that describe the main concepts presented in a document dataset. Furthermore, the knowledge existed in the most informative sentences can be employed to improve the understanding of user and/or community interests. Different approaches have been proposed to extract summaries from unstructured text documents. Some of them are based on the statistical analysis of linguistic features by means of supervised machine learning or data mining methods, such as Hidden Markov models, neural networks and Naive Bayes methods. An appealing research field is the extraction of summaries tailored to the major user interests. In this context, the problem of extracting useful information according to domain knowledge related to the user interests is a challenging task. The main topics have been to study and design of novel data representations and data mining algorithms useful for managing and extracting knowledge from unstructured documents. This thesis describes an effort to investigate the application of data mining approaches, firmly established in the subject of transactional data (e.g., frequent itemset mining), to textual documents. Frequent itemset mining is a widely exploratory technique to discover hidden correlations that frequently occur in the source data. Although its application to transactional data is well-established, the usage of frequent itemsets in textual document summarization has never been investigated so far. A work is carried on exploiting frequent itemsets for the purpose of multi-document summarization so a novel multi-document summarizer, namely ItemSum (Itemset-based Summarizer) is presented, that is based on an itemset-based model, i.e., a framework comprise of frequent itemsets, taken out from the document collection. Highly representative and not redundant sentences are selected for generating summary by considering both sentence coverage, with respect to a sentence relevance score, based on tf-idf statistics, and a concise and highly informative itemset-based model. To evaluate the ItemSum performance a suite of experiments on a collection of news articles has been performed. Obtained results show that ItemSum significantly outperforms mostly used previous summarizers in terms of precision, recall, and F-measure. We also validated our approach against a large number of approaches on the DUC’04 document collection. Performance comparisons, in terms of precision, recall, and F-measure, have been performed by means of the ROUGE toolkit. In most cases, ItemSum significantly outperforms the considered competitors. Furthermore, the impact of both the main algorithm parameters and the adopted model coverage strategy on the summarization performance are investigated as well. In some cases, the soundness and readability of the generated summaries are unsatisfactory, because the summaries do not cover in an effective way all the semantically relevant data facets. A step beyond towards the generation of more accurate summaries has been made by semantics-based summarizers. Such approaches combine the use of general-purpose summarization strategies with ad-hoc linguistic analysis. The key idea is to also consider the semantics behind the document content to overcome the limitations of general-purpose strategies in differentiating between sentences based on their actual meaning and context. Most of the previously proposed approaches perform the semantics-based analysis as a preprocessing step that precedes the main summarization process. Therefore, the generated summaries could not entirely reflect the actual meaning and context of the key document sentences. In contrast, we aim at tightly integrating the ontology-based document analysis into the summarization process in order to take the semantic meaning of the document content into account during the sentence evaluation and selection processes. With this in mind, we propose a new multi-document summarizer, namely Yago-based Summarizer, that integrates an established ontology-based entity recognition and disambiguation step. Named Entity Recognition from Yago ontology is being used for the task of text summarization. The Named Entity Recognition (NER) task is concerned with marking occurrences of a specific object being mentioned. These mentions are then classified into a set of predefined categories. Standard categories include “person”, “location”, “geo-political organization”, “facility”, “organization”, and “time”. The use of NER in text summarization improved the summarization process by increasing the rank of informative sentences. To demonstrate the effectiveness of the proposed approach, we compared its performance on the DUC’04 benchmark document collections with that of a large number of state-of-the-art summarizers. Furthermore, we also performed a qualitative evaluation of the soundness and readability of the generated summaries and a comparison with the results that were produced by the most effective summarizers. A parallel effort has been devoted to integrating semantics-based models and the knowledge acquired from social networks into a document summarization model named as SociONewSum. The effort addresses the sentence-based generic multi-document summarization problem, which can be formulated as follows: given a collection of news articles ranging over the same topic, the goal is to extract a concise yet informative summary, which consists of most salient document sentences. An established ontological model has been used to improve summarization performance by integrating a textual entity recognition and disambiguation step. Furthermore, the analysis of the user-generated content coming from Twitter has been exploited to discover current social trends and improve the appealing of the generated summaries. An experimental evaluation of the SociONewSum performance was conducted on real English-written news article collections and Twitter posts. The achieved results demonstrate the effectiveness of the proposed summarizer, in terms of different ROUGE scores, compared to state-of-the-art open source summarizers as well as to a baseline version of the SociONewSum summarizer that does not perform any UGC analysis. Furthermore, the readability of the generated summaries has also been analyzed

A survey on opinion summarization technique s for social media

The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. The review begins by visualizing a roadmap of machine learning and its applications in healthcare. And then it explores the integration of RL with NLP tasks. We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction. Additionally, ethical considerations and biases in RL-NLP systems are addressed

An In-Depth Review of ChatGPT’s Pros and Cons for Learning and Teaching in Education

As technology progresses, there has been an increasing interest in using Chatbot GPT (Generative Pre-trained Transformer) in education. Chatbot GPT, or ChatGPT, gained one million users within the first week of launching in November 2022 and had amassed over 100 million active users by February 2023. This type of artificial intelligence uses natural language processing to convert it into a user. This paper presents a comprehensive analysis and review of 34 articles published on ChatGPT and its potential impact on education by utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. This review analyzed various studies and articles to examine the strengths and limitations of GPT language models in education from 2018 to the present. The advantages of ChatGPT include its capacity to provide personalized and adaptive learning, instant feedback, and improved accessibility. However, there are potential drawbacks, such as the lack of emotional intelligence, the risk of overreliance on technology, and privacy concerns. This review suggests that ChatGPT has significant promise for education yet reinforces the necessity for further research and careful consideration of possible risks and limitations. Specifically, it pointed out potential invisible manipulations by instructing ChatGPT to answer educationrelated topics. The paper concludes by discussing the implications of ChatGPT for the future of education and emphasizing the need for further research in this field

Adaptive Semantic Annotation of Entity and Concept Mentions in Text

The recent years have seen an increase in interest for knowledge repositories that are useful across applications, in contrast to the creation of ad hoc or application-specific databases. These knowledge repositories figure as a central provider of unambiguous identifiers and semantic relationships between entities. As such, these shared entity descriptions serve as a common vocabulary to exchange and organize information in different formats and for different purposes. Therefore, there has been remarkable interest in systems that are able to automatically tag textual documents with identifiers from shared knowledge repositories so that the content in those documents is described in a vocabulary that is unambiguously understood across applications. Tagging textual documents according to these knowledge bases is a challenging task. It involves recognizing the entities and concepts that have been mentioned in a particular passage and attempting to resolve eventual ambiguity of language in order to choose one of many possible meanings for a phrase. There has been substantial work on recognizing and disambiguating entities for specialized applications, or constrained to limited entity types and particular types of text. In the context of shared knowledge bases, since each application has potentially very different needs, systems must have unprecedented breadth and flexibility to ensure their usefulness across applications. Documents may exhibit different language and discourse characteristics, discuss very diverse topics, or require the focus on parts of the knowledge repository that are inherently harder to disambiguate. In practice, for developers looking for a system to support their use case, is often unclear if an existing solution is applicable, leading those developers to trial-and-error and ad hoc usage of multiple systems in an attempt to achieve their objective. In this dissertation, I propose a conceptual model that unifies related techniques in this space under a common multi-dimensional framework that enables the elucidation of strengths and limitations of each technique, supporting developers in their search for a suitable tool for their needs. Moreover, the model serves as the basis for the development of flexible systems that have the ability of supporting document tagging for different use cases. I describe such an implementation, DBpedia Spotlight, along with extensions that we performed to the knowledge base DBpedia to support this implementation. I report evaluations of this tool on several well known data sets, and demonstrate applications to diverse use cases for further validation

GenAI-powered Social Bots for Crisis Communication: A Systematic Literature Review

Climate change causes ever more natural hazards. In such crisis situations, individuals seek for information about the situations constantly. Due to its ever-growing relevancy in the everyday life, social media are increasingly used to seek and discuss crisis-related information. Social media platforms offer the possibility to deploy social bots (i.e., automated user accounts) that are partly credited to be used for malicious purposes but also considered useful by emergency management agencies (EMAs) to disseminate situational updates and information. Many tasks for which EMAs employ social bots rely on the publication of information in textual form. Recent advancements of generative artificial intelligence (GenAI) offer means to generate texts, images, and videos automatically. Therefore, we explore how social bots can benefit from GenAI in crisis communication. To this end, we conduct a systematic literature review and offer a research agenda to guide future endeavours

SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis

5G is the 5th generation cellular network protocol. It is the state-of-the-art global wireless standard that enables an advanced kind of network designed to connect virtually everyone and everything with increased speed and reduced latency. Therefore, its development, analysis, and security are critical. However, all approaches to the 5G protocol development and security analysis, e.g., property extraction, protocol summarization, and semantic analysis of the protocol specifications and implementations are completely manual. To reduce such manual effort, in this paper, we curate SPEC5G the first-ever public 5G dataset for NLP research. The dataset contains 3,547,586 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on NLP tasks, we use this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task. Our results show the value of our 5G-centric dataset in 5G protocol analysis automation. We believe that SPEC5G will enable a new research direction into automatic analyses for the 5G cellular network protocol and numerous related downstream tasks. Our data and code are publicly available