Document Summarization
195 papers with code • 7 benchmarks • 28 datasets
Automatic Document Summarization is the task of rewriting a document into its shorter form while still retaining its important content. The most popular two paradigms are extractive approaches and abstractive approaches. Extractive approaches generate summaries by extracting parts of the original document (usually sentences), while abstractive methods may generate new words or phrases which are not in the original document.
Benchmarks
These leaderboards are used to track progress in Document Summarization
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Libraries
Use these libraries to find Document Summarization models and implementationsDatasets
CNN/Daily Mail
New York Times Annotated Corpus
Multi-News
NEWSROOM
WikiSum
WikiLingua
Arxiv HEP-TH citation graph
BookSum
WCEP
Latest papers with no code
Non-Parametric Memory Guidance for Multi-Document Summarization
Multi-document summarization (MDS) is a difficult task in Natural Language Processing, aiming to summarize information from several documents.
Mitigating Framing Bias with Polarity Minimization Loss
Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events.
Abstractive Summarization of Large Document Collections Using GPT
This paper proposes a method of abstractive summarization designed to scale to document collections instead of individual documents.
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards
Memory-efficient large language models are good at refining text input for better readability.
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction
Multi-document summarization is a challenging task due to its inherent subjective bias, highlighted by the low inter-annotator ROUGE-1 score of 0. 4 among DUC-2004 reference summaries.
Finding Pragmatic Differences Between Disciplines
Using a corpus of scholarly documents across 19 disciplines and state-of-the-art language modeling techniques, we learn a fixed set of domain-agnostic descriptors for document sections and "retrofit" the corpus to these descriptors (also referred to as "normalization").
Multi-document Summarization: A Comparative Evaluation
This work serves as a reference for future MDS research and contributes to the development of accurate and robust models which can be utilized on demanding datasets with academically and/or scientifically complex data as well as generalized, relatively simple datasets.
Unsupervised Multi-document Summarization with Holistic Inference
SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners.
A Comparative Study of Sentence Embedding Models for Assessing Semantic Variation
In this paper, we compare several recent sentence embedding methods via time-series of semantic similarity between successive sentences and matrices of pairwise sentence similarity for multiple books of literature.
A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data
The exponential growth of textual data has created a crucial need for tools that assist users in extracting meaningful insights.
