Extractive Summarization: Limits, Compression, Generalized Model and Heuristics
Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes existing models in the literature and integrates several dimensions of the summarization, viz., abstractive versus extractive, single versus multi-document, and syntactic versus semantic. Finally, we examine some new and existing single-document summarization algorithms in a single framework and compare with state of the art summarizers on DUC data.
Code (0)
등록된 구현이 없습니다.
Tasks
Document SummarizationExtractive SummarizationmodelMulti-Document SummarizationText SummarizationSimilar Papers 제목 키워드 기반
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization
In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages. Given English gold summaries and docum…
Extractive SummarizationExtractive Text SummarizationSentenceText SummarizationUnsupervised Extractive Summarization by Human Memory Simulation
Summarization systems face the core challenge of identifying and selecting important information. In this paper, we tackle the problem of content selection in unsupervised extractive summarization of long, structured doc…
ArticlesExtractive SummarizationUnsupervised Extractive SummarizationTopic Modeling Based Extractive Text Summarization
Text summarization is an approach for identifying important information present within text documents. This computational technique aims to generate shorter versions of the source text, by including only the relevant and…
Abstractive Text SummarizationExtractive Text SummarizationText SummarizationCharacterizing Prompt Compression Methods for Long Context Inference
Long context inference presents challenges at the system level with increased compute and memory requirements, as well as from an accuracy perspective in being able to reason over long contexts. Recently, several methods…
Neural Extractive Text Summarization with Syntactic Compression
Recent neural network approaches to summarization are largely either selection-based extraction or generation-based abstraction. In this work, we present a neural model for single-document summarization based on joint ex…
Document SummarizationExtractive Text SummarizationText Summarization