https://github.com/ashokurlana/controllable_text_summarization_survey
공식 코드Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey
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Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.
There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey
PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
Echoes from Alexandria: A Large Resource for Multilingual Book Summarization
A General Contextualized Rewriting Framework for Text Summarization
GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
Explicit Sentence Compression for Neural Machine Translation
Analyzing and Evaluating Faithfulness in Dialogue Summarization
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization
Align and Attend: Multimodal Summarization with Dual Contrastive Losses
From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization
Incremental Extractive Opinion Summarization Using Cover Trees
Unsupervised Extractive Opinion Summarization Using Sparse Coding
Heterogeneous Graph Neural Networks for Extractive Document Summarization
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
Shaping Political Discourse using multi-source News Summarization
The Summary Loop: Learning to Write Abstractive Summaries Without Examples
Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
DTELS: Towards Dynamic Granularity of Timeline Summarization
COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
Constructing Contrastive samples via Summarization for Text Classification with limited annotations
Entity-Based Evaluation of Political Bias in Automatic Summarization
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.