Specificity-Based Sentence Ordering for Multi-Document Extractive Risk Summarization
Risk mining technologies seek to find relevant textual extractions that capture entity-risk relationships. However, when high volume data sets are processed, a multitude of relevant extractions can be returned, shifting the focus to how best to present the results. We provide the details of a risk mining multi-document extractive summarization system that produces high quality output by modeling shifts in specificity that are characteristic of well-formed discourses. In particular, we propose a novel selection algorithm that alternates between extracts based on human curated or expanded autoencoded key terms, which exhibit greater specificity or generality as it relates to an entity-risk relationship. Through this extract ordering, and without the need for more complex discourse-aware NLP, we induce felicitous shifts in specificity in the alternating summaries that outperform non-alternating summaries on automatic ROUGE and BLEU scores, and manual understandability and preferences evaluations - achieving no statistically significant difference when compared to human authored summaries.
Code (0)
등록된 구현이 없습니다.
Tasks
Extractive SummarizationSentenceSentence OrderingSpecificitySimilar Papers 제목 키워드 기반
OrderSum: Semantic Sentence Ordering for Extractive Summarization
There are two main approaches to recent extractive summarization: the sentence-level framework, which selects sentences to include in a summary individually, and the summary-level framework, which generates multiple cand…
Extractive SummarizationSentenceSentence OrderingHeterogeneous Graph Neural Networks for Extractive Document Summarization
As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more…
Document SummarizationExtractive Document SummarizationExtractive SummarizationExtractive Text Summarization+1Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects th…
Document SummarizationNews SummarizationMulti-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization
In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word re…
Document SummarizationExtractive SummarizationMulti-Document SummarizationSentenceCAWESumm: A Contextual and Anonymous Walk Embedding Based Extractive Summarization of Legal Bills
Extractive summarization of lengthy legal documents requires an appropriate sentence scoring mechanism. This mechanism should capture both the local semantics of a sentence as well as the global document-level context of…
Document EmbeddingExtractive SummarizationSentenceSentence Embedding+1