On the other hand, the reinforcement learning technique is popular for robotics, andīecoming accessible for the text summarization problem in the last couple of years (Narayan et al., 2018). Query-based summarization problem is an interesting problem in the text summarizationįield. Experiments conducted for the task of sentence fusion and multi-document summarization demonstrate superior performance in comparison with the state-of-the-art techniques available in the literature. Integer Linear Programming has been used to make the final selection of the scored sentences for generating the abstract. A sentence scoring function assisted by an intensification function is used to measure the grammaticality and informativeness of the generated sentences. The advantage of the above representation is that it utilizes alignment information of words between pairs of similar sentences to merge nodes in the Word Graph, and thereby facilitating the generation of sentences with multiple chunks of information. The sentences belonging to the same cluster are then represented using Word Graph, in which words of different sentences are aligned based on their semantic and syntactic similarities. In the first step, the proposed scheme uses SBERT embedding for representing the sentences as fixed-size vectors. The present work proposes a scheme for multi-document abstractive text summarization using node-aligned Word Graph based representation of clustered sentences. We achieved improved performance by adding query embeddings to the input of the model, by using BART as an alternative language model, and by using clustering methods to extract key information at utterance level before feeding the text into summarization models. Lastly, we compare the performance of our baseline models with BART, a state-of-the-art language model that is effective for summarization. We started with HMNet\cite with an intermediate clustering step. In this paper, we experiment with different approaches to improve the performance of query-based meeting summarization. The ability to automatically summarize meetings and to extract key information could greatly increase the efficiency of our work and life. We conclude that abstractive methods can effectively synthesise the rich information contained in sentiment-bearing topics.Īutomatic meeting summarization is becoming increasingly popular these days. In addition, when comparing extractive and abstractive labels, our evaluation shows that our best performing abstractive method is able to provide more topic information coverage in fewer words, at the cost of generating less grammatical labels than the extractive method. Our experimental results on three real-world datasets show that both the extractive and abstractive approaches outperform four strong baselines in terms of facilitating topic understanding and interpretation. To our knowledge, we are the first to study the problem of labelling sentiment-bearing topics. The abstractive approach instead addresses aspect-sentiment co-coverage by using sentence fusion to generate a sentential label that includes relevant content from multiple sentences. ![]() The extractive approach uses a sentence ranking algorithm for label selection which for the first time jointly optimises topic-sentence relevance as well as aspect-sentiment co-coverage. ![]() Both approaches rely on a novel mechanism to automatically learn the relevance of each sentence in a corpus to sentiment-bearing topics extracted from that corpus. We propose two approaches to the problem, one extractive and the other abstractive. This paper tackles the problem of automatically labelling sentiment-bearing topics with descriptive sentence labels.
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