Query-based Summarization Research Articles

A Hybrid Query-Based Extractive Text Summarization Based on K-Means and Latent Dirichlet Allocation Techniques

A Hybrid Query-Based Extractive Text Summarization Based on K-Means and Latent Dirichlet Allocation Techniques

Query-based video summarization with multi-label classification network

Query-based video summarization with multi-label classification network

Query-Based Video Summarization System Based on Light Weight Deep Learning Model

Query-Based Video Summarization System Based on Light Weight Deep Learning Model

Efficient semantic summary graphs for querying large knowledge graphs

Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the development of efficient tools for allowing end users to extract useful insights from these sources of knowledge. In such a context, reducing the size of a Resource Description Framework (RDF) graph while preserving all information can speed up query engines by limiting data shuffle, especially in a distributed setting. This paper presents two algorithms for RDF graph summarization: Grouping Based Summarization (GBS) and Query Based Summarization (QBS). The latter is an optimized and lossless approach for the former method. We empirically study the effectiveness of the proposed lossless RDF graph summarization to retrieve complete data, by rewriting an RDF Query Language called SPARQL query with fewer triple patterns using a semantic similarity. We conduct our experimental study in instances of four datasets with different sizes. Compared with the state-of-the-art query engine Sparklify executed over the original RDF graphs as a baseline, QBS query execution time is reduced by up to 80% and the summarized RDF graph is decreased by up to 99%.

PQPS: Prior-Art Query-Based Patent Summarizer Using RBM and Bi-LSTM

A prior-art search on patents ascertains the patentability constraints of the invention through an organized review of prior-art document sources. This search technique poses challenges because of the inherent vocabulary mismatch problem. Manual processing of every retrieved relevant patent in its entirety is a tedious and time-consuming job that demands automated patent summarization for ease of access. This paper employs deep learning models for summarization as they take advantage of the massive dataset present in the patents to improve the summary coherence. This work presents a novel approach of patent summarization named PQPS: prior-art query-based patent summarizer using restricted Boltzmann machine (RBM) and bidirectional long short-term memory (Bi-LSTM) models. The PQPS also addresses the vocabulary mismatch problem through query expansion with knowledge bases such as domain ontology and WordNet. It further enhances the retrieval rate through topic modeling and bibliographic coupling of citations. The experiments analyze various interlinked smart device patent sample sets. The proposed PQPS demonstrates that retrievability increases both in extractive and abstractive summaries.

A Highly Scalable Method for Extractive Text Summarization Using Convex Optimization

The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally hard integer programming problem. The objective function is highly symmetric, being invariant under unitary transformations of the text representations. Another key idea is to replace the constraint on the number of sentences in the summary with a convex surrogate. For solving the program we have designed a specific projected gradient descent algorithm and analyzed its performance in terms of execution time and quality of the approximation. Using the datasets DUC 2005 and Cornell Newsroom Summarization Dataset, we have shown empirically that the algorithm can provide competitive results for single document summarization and multi-document query-based summarization. On the Cornell Newsroom Summarization Dataset, it ranked second among the unsupervised methods tested. For the more challenging task of multi-document query-based summarization, the method was tested on the DUC 2005 Dataset. Our algorithm surpassed the other reported methods with respect to the ROUGE-SU4 metric, and it was at less than 0.01 from the top performing algorithms with respect to ROUGE-1 and ROUGE-2 metrics.

Knowledge-based sentence semantic similarity: algebraical properties

Determining the extent to which two text snippets are semantically equivalent is a well-researched topic in the areas of natural language processing, information retrieval and text summarization. The sentence-to-sentence similarity scoring is extensively used in both generic and query-based summarization of documents as a significance or a similarity indicator. Nevertheless, most of these applications utilize the concept of semantic similarity measure only as a tool, without paying importance to the inherent properties of such tools that ultimately restrict the scope and technical soundness of the underlined applications. This paper aims to contribute to fill in this gap. It investigates three popular WordNet hierarchical semantic similarity measures, namely path-length, Wu and Palmer and Leacock and Chodorow, from both algebraical and intuitive properties, highlighting their inherent limitations and theoretical constraints. We have especially examined properties related to range and scope of the semantic similarity score, incremental monotonicity evolution, monotonicity with respect to hyponymy/hypernymy relationship as well as a set of interactive properties. Extension from word semantic similarity to sentence similarity has also been investigated using a pairwise canonical extension. Properties of the underlined sentence-to-sentence similarity are examined and scrutinized. Next, to overcome inherent limitations of WordNet semantic similarity in terms of accounting for various Part-of-Speech word categories, a WordNet “All word-To-Noun conversion” that makes use of Categorial Variation Database (CatVar) is put forward and evaluated using a publicly available dataset with a comparison with some state-of-the-art methods. The finding demonstrates the feasibility of the proposal and opens up new opportunities in information retrieval and natural language processing tasks.

Effective Deep Neural Network Method based Sentimental Analysis for Social Media Health Care Information

With the advent of natural language processing and machine learning techniques, Sentimental Analysis (SA) is receiving attention among various communities due to interpreting and classifying emotions from subjective data. The way of categorizing the emotions as positive, negative, and neutral enables the brand providers to know about their intention and reaction to their products. Existing researches have been focused on text summarization, feature reduction, and sentiment prediction separately. In this paper, all the approaches are integrated to provide a novel sentimental analysis framework for classifying the patients' emotions towards the medical facility through Twitter, Facebook, and other social media. The main aim of the work is manifold. The proposed paper performs preprocessing at the outset, which includes word segmentation, tokenization, TF-IDF, and stemming. First, the text summarization is performed by applying query-based summarization, which adopts a Deep Neural Network (DNN) for encoding the input data encompassing text and icon to convert into a fixed size at the final state, followed by decoding the encoded text and icon using the unidirectional DNN for creating the summary of the input document. Second, to improve scalability, feature reduction is performed using the effective Genetic Algorithm. The proposed system is experimented with using the real-time datasets from social media platforms to analyze the proposed healthcare sentimental analysis model. The parameters such as accuracy, F-score, recall rate, and precision are chosen to analyze the performance against existing sentimental analysis models.

A unified model for egocentric video summarization: an instance-based approach

Video summarization generates compact representations of videos in the form of summaries. The proposed framework is a unified model for instance-driven egocentric video summarization addressing generic and query-based summarization along with multi-video summarization. The model employs deep learning for object detection and semantic web technologies in the form of ontologies for query inferences. Combining user preferences in the form of object queries has aided in producing summaries that are subjective in nature. Quantitative evaluations performed on two novel datasets namely, ‘vehicle expo’ and ‘academic inspection’ prove that the proposed framework produces remarkable results with the employment of instance-driven modules for summarization. Additional experimental analysis for shot boundary detection have been conducted based on proposed method and conventional methods establishing the significance of the instance-based model. Moreover, qualitative evaluations further ensure that the summaries are concise, representative, diverse and semantically relevant further substantiating the need for instance-driven models in video summarization.

An assortment of Query Based Summarization technique (QBS) – A Study

This paper presents latest techniques followed in Query based single and multi-document summarization. In this era, we see an exponential growth of text data in all fields, it is rigid to get vital and precise information on time. Automatic Text Summarization is obligatory to reduce the stint to analyses elongated and complicated document. Text summarization is categorized based on many factors, each has its own merits and demerits. Query based summarization has attracted increasing interest due to its usage in many applications. This survey includes query-based summarization using graph, hyper graph, RAKE algorithm and Linguistic knowledge. DUC is the popular data set used in most text summarization, but due to its inability to suit various kinds of queries, some authors have used their own data set.

문장 랭킹 스코어와 그래프 기법을 사용한 질의 기반 생성 요약 모델

기본적인 생성 요약 모델은 문서 내 모든 중요 내용을 포함하는 짧은 요약문을 생성하는 것을 목표로 한다. 반면, 질의(Query) 기반 생성 요약 모델의 경우 문서 내에서 질의와 관련된 정보를 요약해야 한다. 기존의 질의 기반 요약 모델은 문서 내 단어들과 질의문 간의 어텐션(Attention) 메커니즘을 통해 단어의 가중치를 계산하고 이를 기반으로 문장의 중요도를 계산한다. 이러한 방식은 문서의 전체적인 문맥정보를 반영하기 어렵다는 단점이 있다. 본 논문에서는 이러한 문제를 문장 랭킹 스코어와 문장 단위 그래프 구조를 만들어 문장의 중요도뿐만 아니라 문맥 정보를 반영하여 생성 요약의 성능을 향상시킬 수 있는 새로운 생성 요약 기법을 제안한다. 실험으로 살펴본 최종 제안 모델의 성능은 같은 데이터를 사용하는 선행 모델 대비 ROUGE-1 1.44%p, ROUGE-L 0.52%p의 향상된 성능을 보인다.

QBSUM: A large-scale query-based document summarization dataset from real-world applications

Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summarization are short in numbers and existing datasets are also limited in both scale and quality. Moreover, to the best of our knowledge, there is no publicly available dataset for Chinese query-based document summarization. In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. We also propose multiple unsupervised and supervised solutions to the task and demonstrate their high-speed inference and superior performance via both offline experiments and online A/B tests. The QBSUM dataset is released in order to facilitate future advancement of this research field.

Improvement of query-based text summarization using word sense disambiguation

In this paper, a query-based text summarization method is proposed based on common sense knowledge and word sense disambiguation. Common sense knowledge is integrated here by expanding the query terms. It helps in extracting main sentences from text document according to the query. Query-based text summarization finds semantic relatedness score between query and input text document for extracting sentences. The drawback with current methods is that while finding semantic relatedness between input text and query, in general they do not consider the sense of the words present in the input text sentences and the query. However, this particular method can enhance the summary quality as it finds the correct sense of each word of a sentence with respect to the context of the sentence. The correct sense for each word is being used while finding semantic relatedness between input text and query. To remove similar sentences from summary, similarity measure is computed among the selected sentences. Experimental result shows better performance than many baseline systems.

Query-based summarization of discussion threads

AbstractIn this paper, we address query-based summarization of discussion threads. New users can profit from the information shared in the forum, Please check if the inserted city and country names in the affiliations are correct. if they can find back the previously posted information. However, discussion threads on a single topic can easily comprise dozens or hundreds of individual posts. Our aim is to summarize forum threads given real web search queries. We created a data set with search queries from a discussion forum’s search engine log and the discussion threads that were clicked by the user who entered the query. For 120 thread–query combinations, a reference summary was made by five different human raters. We compared two methods for automatic summarization of the threads: a query-independent method based on post features, and Maximum Marginal Relevance (MMR), a method that takes the query into account. We also compared four different word embeddings representations as alternative for standard word vectors in extractive summarization. We find (1) that the agreement between human summarizers does not improve when a query is provided that: (2) the query-independent post features as well as a centroid-based baseline outperform MMR by a large margin; (3) combining the post features with query similarity gives a small improvement over the use of post features alone; and (4) for the word embeddings, a match in domain appears to be more important than corpus size and dimensionality. However, the differences between the models were not reflected by differences in quality of the summaries created with help of these models. We conclude that query-based summarization with web queries is challenging because the queries are short, and a click on a result is not a direct indicator for the relevance of the result.

Extractive multi-document summarization based on textual entailment and sentence compression via knapsack problem

AbstractBy increasing the amount of data in computer networks, searching and finding suitable information will be harder for users. One of the most widespread forms of information on such networks are textual documents. So exploring these documents to get information about their content is difficult and sometimes impossible. Multi-document text summarization systems are an aid to producing a summary with a fixed and predefined length, while covering the maximum content of the input documents. This paper presents a novel method for multi-document extractive summarization based on textual entailment relations and sentence compression via formulating the problem as a knapsack problem. In this approach, sentences of documents are ranked according to the extended Tf-Idf method, then entailment scores of selected sentences are computed. Through these scores, the final score of each sentence is calculated. Finally, by decreasing the lengths of sentences via sentence compression, the problem has been solved by greedy and dynamic Programming approaches to the knapsack problem. Experiments on standard summarization datasets and evaluating the results based on the Rouge system show that the suggested method, according to the best of our knowledge, has increased F-measure of query-based summarization systems by two per cent and F-measure of general summarization systems by five per cent.

Using Unsupervised Deep Learning for Automatic Summarization of Arabic Documents

Traditional Arabic text summarization (ATS) systems are based on bag-of-words representation, which involve a sparse and high-dimensional input data. Thus, dimensionality reduction is greatly needed to increase the power of features discrimination. In this paper, we present a new method for ATS using variational auto-encoder (VAE) model to learn a feature space from a high-dimensional input data. We explore several input representations such as term frequency (tf), tf-idf and both local and global vocabularies. All sentences are ranked according to the latent representation produced by the VAE. We investigate the impact of using VAE with two summarization approaches, graph-based and query-based approaches. Experiments on two benchmark datasets specifically designed for ATS show that the VAE using tf-idf representation of global vocabularies clearly provides a more discriminative feature space and improves the recall of other models. Experiment results confirm that the proposed method leads to better performance than most of the state-of-the-art extractive summarization approaches for both graph-based and query-based summarization approaches.

QMOS: Query-based multi-documents opinion-oriented summarization

Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews.QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon.On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users’ needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods.

Searching web documents using a summarization approach

Purpose The purpose of this paper is to introduce a summarization method to enhance the current web-search approaches by offering a summary of each clustered set of web-search results with contents addressing the same topic, which should allow the user to quickly identify the information covered in the clustered search results. Web search engines, such as Google, Bing and Yahoo!, rank the set of documents S retrieved in response to a user query and represent each document D in S using a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e. assisting its users to quickly identify results of interest. These snippets are inadequate in providing distinct information and capture the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user’s intended request without requiring additional information. Furthermore, a document title is not always a good indicator of the content of the corresponding document either. Design/methodology/approach The authors propose to develop a query-based summarizer, called QSum, in solving the existing problems of Web search engines which use titles and abstracts in capturing the contents of retrieved documents. QSum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user’s time and effort in searching for specific information of interest by skipping the step to browse through the retrieved documents one by one. Findings Experimental results show that QSum is effective and efficient in creating a high-quality summary for each cluster to enhance Web search. Originality/value The proposed query-based summarizer, QSum, is unique based on its searching approach. QSum is also a significant contribution to the Web search community, as it handles the ambiguous problem of a search query by creating summaries in response to different interpretations of the search which offer a “road map” to assist users to quickly identify information of interest.

Query-based multi-documents summarization using linguistic knowledge and content word expansion

In this paper, a query-based summarization method, which uses a combination of semantic relations between words and their syntactic composition, to extract meaningful sentences from document sets is introduced. The problem with current statistical methods is that they fail to capture the meaning when comparing a sentence and a user query; hence there is often a conflict between the extracted sentences and users' requirements. However, this particular method can improve the quality of document summaries because it is able to avoid extracting a sentence whose similarity with the query is high but whose meaning is different. The method is executed by computing the semantic and syntactic similarity of the sentence-to-sentence and sentence-to-query. To reduce redundancy in summary, this method uses the greedy algorithm to impose diversity penalty on the sentences. In addition, the proposed method expands the words in both the query and the sentences to tackle the problem of information limit. It bridges the lexical gaps for semantically similar contexts that are expressed using different wording. The experimental results display that the proposed method is able to improve performance compared with the participating systems in DUC 2006. The experimental results also showed that the proposed method demonstrates better performance as compared to other existing techniques on DUC 2005 and DUC 2006 datasets.

Enhancing web search by using query-based clusters and multi-document summaries

Current web search engines, such as Google, Bing, and Yahoo!, rank the set of documents SD retrieved in response to a user query and display each document D in SD with a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e., assisting its users to quickly identify results of interest, if they exist. These snippets are inadequate in providing distinct information and capturing the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user's intended request without requiring additional inputs. Furthermore, a document title is not always a good indicator of the content of the corresponding document. All of these design problems can be solved by our proposed query-based cluster and summarizer, called $$Q_{Sum}$$QSum. $$Q_{Sum}$$QSum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user's time and effort in searching for specific information of interest without having to browse through the documents one by one. Experimental results show that $$Q_{Sum}$$QSum is effective and efficient in generating a high-quality summary for each cluster of documents on a specific topic.