Abstractive Multi-Document Summarization via Phrase Selection and Merging

Abstract

We propose an abstraction-based multidocument summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-ofthe-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.

Abstractive Multi-document Summarization with Semantic Information Extraction

“…The pyramid evaluation metric involves semantic matching of summary content units (SCUs) so as to recognize alternate realizations of the same meaning, which is a better metric for the abstractive summary evaluation. Since the manual pyramid evaluation is time-consuming and the evaluation results can't be reproducible with different groups of assessors, we use the automated version of pyramid proposed in (Passonneau et al, 2013) and adopt the same setting as in (Bing et al, 2015). Table 3 shows the evaluation results of our system and the three baseline systems on DUC 2007.…”

“…Moreover, texts can be generated efficiently from the BSUs network. Another recent abstractive summarization method generates new sentences by selecting and merging phrases from the input documents (Bing et al, 2015). It first extracts noun phrases and verb-object phrases from the input documents, and then calculates saliency scores for them.…”

Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization

“…For example Li (2015) and Bing et al (2015) use an earlier version of AP based on distributional semantics (Passonneau et al, 2013) to evaluate abstractive multi-document summarization.…”

Enumeration of Extractive Oracle Summaries

“…Recently, compressive and abstractive summarization are attracting attention (e.g., Almeida and Martins (2013), Qian and Liu (2013), Yao et al (2015), Banerjee et al (2015), Bing et al (2015)). However, extractive summarization remains a primary research topic because the linguistic quality of the resultant summaries is guaranteed, at least at the sentence level, which is a key requirement for practical use (e.g., , Hong et al (2015), Yogatama et al (2015), Parveen et al (2015)).…”