W ikipedia Current Events Summarization using Particle Swarm Optimization (2021)

Abstract: This paper proposes a method to summarize news events from multiple sources. We pose event summarization as a clustering-based optimization problem and solve it using particle swarm optimization. The proposed methodology uses the search capability of particle swarm optimization, detecting the number of clusters automatically. Experiments are conducted with the Wikipedia Current Events Portal dataset and evaluated using the well-known ROUGE-1, ROUGE-2, and ROUGE-L scores. The obtained results show the efficacy of the proposed methodology over the state-of-the-art methods. It attained improvement of 33.42%, 81.75%, and 57.58% in terms of ROUGE-1, ROUGE-2, and ROUGE-L, respectively.

## introduction the continuously rising amount of text data makes analyzing and comprehending textual files tiresome as technology progresses in a fast-changing fashion. capturing important information from large documents is a time-consuming and labor-intensive job from the reader's perspective. a large number of documents must be handled quickly, and a large amount of text data necessitates the use of text and document summarization algorithms. however, the focus has been on single-document summarization both for extractive and abstractive variants with comparatively little advancements in multi-document summarization. multi-document summarization techniques are becoming paramount in recent years. there are several real life applications of multi document summarization like : scientific summarization @xcite @xcite , news summarization @xcite , email thread summarization @xcite , summarization of product reviews @xcite , course feedback summarization @xcite , wikipedia article generation @xcite , summarization of medical documents @xcite . deep learning has gained a huge amount of attention in recent years as a result of its success in computer vision @xcite , natural language processing @xcite , and multi-modal applications @xcite @xcite . researchers use deep learning to solve challenging problems because of its capacity to capture highly nonlinear data relationships. deep learning-based models have recently been used in multi-document summarization @xcite @xcite , advancing the field of text summarization and allowing models to improve their performances. attempts to utilise big deep learning models, which have considerably improved the state-of-the-art for different supervised natural language processing tasks, however, are hampered by a shortage of large datasets, making a comprehensive evaluation impossible. even with larger datasets, compute resources and the corresponding training time might also pose a challenge in case of mds (multi-document summarization) since several documents have to be processed. moreover, for single and multi-document summarization, meta-heuristics algorithms have shown good results in previous studies @xcite @xcite . in this work, we have proposed a meta-heuristic optimization technique-based multi-document summarization methodology using wikipedia current events portal (wcep) dataset introduced in association for computational linguistics (acl), 2020 @xcite . major contributions of this work are as follows: ## related work to solve multi-document summarization, nonneural and neural network-based methods have been used in the literature. non-neural approaches have been widely used in the literature for multi-document summarization. in @xcite , authors have used query relevance and maximum marginal relevance to accomplish text summarization. they utilized the maximum marginal relevance to maintain anti-redundancy in generated summary. authors of @xcite proposed a clustering-based approach in which summary is generated using cluster centroid. apart from this, they proposed evaluation techniques using subsumption and sentence utility for single and multi-document summarization. in @xcite , an unsupervised graphical method, lexrank, has been proposed for text summarization. here, the proposed method accomplishes sentence scoring using the graph-based method. lexrank finds the important sentences utilizing eigenvector centrality of graph representation denoting sentences. authors of @xcite have proposed the textrank method for text summarization; this is based on page ranking methodology. in @xcite , a generative probabilistic methodology to summarize multiple documents is proposed. here, the authors have proposed a way of constructing a sequence of models using a frequency-based model. in @xcite , authors have developed a method 'sum-mons' that combines information from various news articles and converts it into a summary. in @xcite , multi-document summarization is accomplished by finding similar elements across texts from different documents. a graphbased summarization technique, namely 'opinosis' introduced in @xcite , generates a precise abstractive summary from the redundant opinion. a word-level and sentence-level ranking based on various indicators of importance, keyword extraction, and phrase-level salience @xcite ) @xcite , greedy heuristics on relation graphs and embedding @xcite has been used to solve the multi-document summarization. nowadays, supervised learning is used to solve extractive and abstractive summarization problems. but, limitation of supervised approaches is that it requires a huge amount of data for training. an attention with encoder-decoder based recurrent neural network is introduced in @xcite . here, authors have carried out abstractive summarization over duc and cnn/daily mail datasets. in @xcite , authors have proposed a data-driven approach with a deep neural network that incorporates the continuous sentence features. they developed an architecture consisting of a hierarchical document encoder and an attention-based extractor. a sentence rankingbased approach for single document summarization is explored in @xcite . here, authors have proposed a training methodology to optimize the rouge evaluation metric using reinforcement learning. a conditional recurrent neural network has been employed for abstractive summarization in @xcite . here, the convolutional attention-based encoder ensures the conditioning of the input sequence that helps the decoder to focus on relevant input words at each time step of the summary generation. in @xcite , an extractive summarization of the document has been carried out using contrasting recurrent neural network-based architecture. the proposed method classifies the sentences in a sequential way that decides whether a sentence should be accepted or rejected to be included in the summary. further, a sentence selector selects a single sentence at a time in random order to form the summary. a sequence to sequence architecture for abstractive summarization has been introduced in @xcite . authors have proposed a pointer generatorbased sequence to sequence model that can copy a word from the source text that helps in generating an accurate summary. in @xcite , an intra-attention mechanism has been introduced that attends the input sequence and generates the output separately in a continuous manner. the authors have also proposed a new training methodology that utilizes reinforcement learning and supervised word prediction. standard word prediction is coupled with rl's global sequence prediction training, resulting in more comprehensible summaries. author of @xcite proposed a architecture to learn discourse structure of the documents. apart from these, they also employed an attentive discourse-aware decoder that can summarize single and multiple documents. in @xcite , abstractive summarization has been accomplished using deep communicating agents in the encoder-decoder model. here, the deep communicating agents divide the long documents into smaller parts and assign them to different collaborative agents. the collaborative agents work as agents connected through a single decoder which trains end-to-end using reinforcement learning to generate a coherent and accurate summary. in @xcite , authors have introduced a data-efficient content selector that finds the phrase in the input document that is important for the summary. this selector is employed as bottom-up attention to constraining the model to similar phrases. the limitation of the supervised approach (deep learning model) is that it needs a huge amount of data for learning. we often don't have enough data to train a supervised model in many instances. motivated by this, we present an unsupervised method for summarizing events in an extractive way from recent news, which we evaluate on the wcep dataset @xcite . it contains daily news events and their corresponding summaries. the proposed approach does not require massive data, and it has consistent performance irrespective of dataset size. ## proposed methodology this section has an overview of the proposed methodology. fig 1 and algorithm 1 illustrate the steps and pseudo-code, respectively. the notations used in this section are defined in the proposed methodology is based on a natural phenomenon; at the end of its execution, it generates a set of solutions. we get a set of optimal solutions at the end. here, a solution is made up of a group of sentence clusters that have been optimized (particle). particle swarm optimization @xcite is a famous nature-inspired method that was designed inspired by the social behavior of bird flocks. it's a population-based method of searching. the method maintains a population of particles. every particle in this diagram represents a viable optimization solution. a swarm comprises numerous alternative solutions to an optimization issue known as particles in the pso framework. the pso algorithm's goal, in this case, is to find the optimal particle position that produces the best fitness value in terms of the objective function. we used a pso-based clustering approach with k-means clustering to seed the original swarm. it entails the following procedures: ## result and discussions this section has a detailed discussion on results obtained and their analysis. we have shown the obtained score in ## conclusion and future works this paper presents a method of wikipedia current event summarization using a particle swarm optimization-based clustering methodology. we utilized the search capability of particle swarm optimization as an underlying optimization strategy, an evolutionary algorithm. the proposed method detects the number of clusters automatically. the different feature has been employed for sentence scoring within-cluster and to form the final summary. the efficacy of the proposed method has been tested on the wcep dataset. the obtained results show the effectiveness of the proposed method over state-of-the-art methods. compared to the best method among the state-of-the-art, the proposed method has the improvement of 33.42%, 81.75%, and 57.58% in terms of rouge-1, rouge-2, and rouge-l, respectively. in the future, this work can be extended using the ensembling of the clustering technique. apart from that, more sophisticated feature word mover's distance, bert similarity, and textual entailment can be utilized for a summary generation.