Minh-Tien Nguyen

TSum4act: A Framework for Retrieving and Summarizing Actionable Tweets During a Disaster for Reaction

Social networks (e.g. Twitter) have been proved to be an almost real-time mean of information spread, thus they can be exploited as a valuable channel of information for emergencies (e.g. disasters) during which people need updated information for suitable reactions. In this paper, we present TSum4act, a framework designed to tackle the challenges of tweets (e.g. diversity, large volume, and noise) for disaster responses. The objective of the framework is to retrieve actionable tweets (e.g. casualties, cautions, and donations) that were posted during disasters. For this purpose, the framework first identifies informative tweets to remove noise; then assigns informative tweets into topics to preserve the diversity; next summarizes the topics to be compact; and finally ranks the results for user’s faster scan. In order to improve the performance, we proposed to incorporate event extraction for enriching the semantics of tweets. TSum4act has been successfully tested on Joplin tornado dataset of 230.535 tweets and the completeness of 0.58 outperformed 17%, of the retweet baseline’s.

SoRTESum: A Social Context Framework for Single-Document Summarization

The combination of web document contents, sentences and users’ comments from social networks provides a viewpoint of a web document towards a special event. This paper proposes a framework named SoRTESum to take advantage of information from Twitter viz. Diversity and reflection of document content to generate high-quality summaries by a novel sentence similarity measurement. The framework first formulates sentences and tweets by recognizing textual entailment (RTE) relation to incorporate social information. Next, they are modeled in a Dual Wing Entailment Graph, which captures the entailment relation to calculate the sentence similarity based on mutual reinforcement information. Finally, important sentences and representative tweets are selected by a ranking algorithm. By incorporating social information, SoRTESum obtained improvements over state-of-the-art unsupervised baselines e.g., Random, SentenceLead, LexRank of 0.51 %–8.8 % of ROUGE-1 and comparable results with strong supervised methods e.g., L2R and CrossL2R trained by RankBoost for single-document summarization.

SoLSCSum: A Linked Sentence-Comment Dataset for Social Context Summarization

This paper presents a dataset named SoLSCSum for social context summarization. The dataset includes 157 open-domain articles along with their comments collected from Yahoo News. The articles and their comments were manually annotated by two annotators to extract standard summaries. The inter-annotator agreement is 74.5% and Cohens Kappa is 0.5845. To illustrate the potential use of our dataset, a learning to rank model was trained by using a set of local and cross features. Experimental results demonstrate that: (1) our model trained by Ranking SVM obtains significant improvements from 5.5% to 14.8% of ROUGE-1 over state-of-the-art baselines in document summarization and (2) our dataset can be used to train summary methods such as SVM.

Intra-relation or inter-relation?

A novel ranking framework for social context summarization is proposed.The framework relies on the reinforcement support of social information.14 features in two groups: distance and statistical are proposed.A new open-domain dataset is created and manually annotated.Combining intra-relation and inter-relation benefits the summarization. Traditional summarization methods only use the internal information of a Web document while ignoring its social information such as tweets from Twitter, which can provide a perspective viewpoint for readers towards an event. This paper proposes a framework named SoRTESum to take the advantages of social information such as document content reflection to extract summary sentences and social messages. In order to do that, the summarization was formulated in two steps: scoring and ranking. In the scoring step, the score of a sentence or social message is computed by using intra-relation and inter-relation which integrate the support of local and social information in a mutual reinforcement form. To calculate these relations, 16 features are proposed. After scoring, the summarization is generated by selecting top m ranked sentences and social messages. SoRTESum was extensively evaluated on two datasets. Promising results show that: (i) SoRTESum obtains significant improvements of ROUGE-scores over state-of-the-art baselines and competitive results with the learning to rank approach trained by RankBoost and (ii) combining intra-relation and inter-relation benefits single-document summarization.

Learning to Summarize Web Documents Using Social Information

This paper presents a method named SoSVMRank, which integrates the social information of a Web document to generate a high-quality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which the order of a sentence or comment was determined by its informative information. The informative information was measured by a set of local and social features in which the social features were exploited to support the local ones when modeling a sentence or comment. To enrich information, new features were also proposed. After ranking, top m ranked sentences and comments were selected as the summarization. Our method was extensively evaluated on two datasets. Promising results indicate that: (1) by using new features, our method achieves improvements in both ROUGE-1 and ROUGE-2 of the summarization over state-of-the-art baselines and (2) integrating social information benefits the summarization.

Recognizing Textual Entailment in Vietnamese Text: An Experimental Study

This paper proposes a model which utilizes Support Vector Machines (SVMs) - a machine learning approach for recognizing textual entailment in Vietnamese text, including three steps: (1) feature extraction, (2) training and (3) judgement by voting. In the first step, many features (e.g., Euclidean distance, Cosine, if-idf, etc) were extracted to train three classification models for the second step. The final step judged whether there is an entailment relation between a text and a hypothesis (another text can be plausibly inferred from the original one) or not. To improve the recognition quality, a combination of classifiers was proposed under voting method as human judgement on Vietnamese version of RTE-3. By using voting, our approach obtained significant improvements (from 1.2% to 9.4% of F-score) in comparison with baselines and ensemble methods, e.g. AdaBoost and Bagging.

Lexical-Morphological Modeling for Legal Text Analysis

In the context of the Competition on Legal Information Extraction/Entailment (COLIEE), we propose a method comprising the necessary steps for finding relevant documents to a legal question and deciding on textual entailment evidence to provide a correct answer. The proposed method is based on the combination of several lexical and morphological characteristics, to build a language model and a set of features for Machine Learning algorithms. We provide a detailed study on the proposed method performance and failure cases, indicating that it is competitive with state-of-the-art approaches on Legal Information Retrieval and Question Answering, while not needing extensive training data nor depending on expert produced knowledge. The proposed method achieved significant results in the competition, indicating a substantial level of adequacy for the tasks addressed.

VnLoc: A Real -- Time News Event Extraction Framework for Vietnamese

Event Extraction is a complex and interesting topic in Information Extraction that includes event extraction methods from free text or web data. The result of event extraction systems can be used in several fields such as risk analysis systems, online monitoring systems or decide support tools. In this paper, we introduce a method that combines lexico -- semantic and machine learning to extract event from Vietnamese news. Furthermore, we concentrate to describe event online monitoring system named VnLoc based on the method that was proposed above to extract event in Vietnamese language. Besides, in experiment phase, we have evaluated this method based on precision, recall and F1 measure. At this time of experiment, we on investigated on three types of event: FIRE, CRIME and TRANSPORT ACCIDENT.

DESRM: a disease extraction system for real-time monitoring

In this paper, we proposed a method that combines semantic rules and machine learning to extract infectious disease events in Vietnamese electronic news for a real-time monitoring system of spreading status. Our method includes two important steps: detecting disease events from unstructured text and extracting information of the disease event. The detection phrase uses semantic rules and machine learning to detect a disease event; in the later step, named entity recognition NER, rules, and dictionaries are utilised to capture the events information. The performance of the two steps has F-score of 77.33% 2.36% better than the baselines and 91.89% 4.31% better than the baselines correspondingly. The promising results from the comparisons showed that our method is suitable for extracting disease events in Vietnamese text.

Extraction of disease events for a real-time monitoring system

In this paper, we propose a method that uses both semantic rules and machine learning to extract infectious disease events in Vietnamese electronic news, which can be used in a real-time system of monitoring the spread of diseases. Our method contains two important steps: detecting disease events from unstructured data and extracting information of the disease events. The event detection uses semantic rules and machine learning to detect a disease event; in the later step, Name Entity Recognition (NER), rules, and dictionaries are used to capture the events information. The performance of detection step is ≈77,33% (F-score) and the precision of extraction step is ≈91,89%. These results are better that those of the experiments in which rules were not used. This indicates that our method is suitable for extracting disease events in Vietnamese text.