Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
| Autor: | Boi Faltings, Diego Antognini |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language business.industry Computer science Byte Machine Learning (stat.ML) computer.software_genre Automatic summarization Graph Machine Learning (cs.LG) Statistics - Machine Learning Multi-document summarization Leverage (statistics) Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing Sentence Semantic relation |
| Popis: | Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on hand-crafted features, which are domain-dependent and hard to craft, or additional annotated data, which is costly to gather. To overcome these limitations, we present a novel method, which makes use of two types of sentence embeddings: universal embeddings, which are trained on a large unrelated corpus, and domain-specific embeddings, which are learned during training. To this end, we develop SemSentSum, a fully data-driven model able to leverage both types of sentence embeddings by building a sentence semantic relation graph. SemSentSum achieves competitive results on two types of summary, consisting of 665 bytes and 100 words. Unlike other state-of-the-art models, neither hand-crafted features nor additional annotated data are necessary, and the method is easily adaptable for other tasks. To our knowledge, we are the first to use multiple sentence embeddings for the task of multi-document summarization. 10 pages, 4 tables, 1 figure, Accepted at 2019 Empirical Methods in Natural Language Processing - Workshop on New Frontiers in Summarization |
| Databáze: | OpenAIRE |
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