https://github.com/stangelid/qt
공식 코드Extractive Opinion Summarization in Quantized Transformer Spaces
Papers with Code 아카이브
Text Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 12
- 평가표
- 37
- 보존된 평가 기록
- 233
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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/stevenlau6/bigsurvey
공식 코드Generating a Structured Summary of Numerous Academic Papers: Dataset and Method
https://github.com/stevenlau6/findsum
공식 코드https://github.com/suhara/sourcesum
공식 코드https://github.com/swarnahub/summarizationprograms
공식 코드Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
https://github.com/swaroop-nath/reward-approx-social-choice-opp-summ
공식 코드https://github.com/tafseer-nayeem/BengaliSummarization
공식 코드Unsupervised Abstractive Summarization of Bengali Text Documents
https://github.com/tagoyal/factuality-datasets
공식 코드Annotating and Modeling Fine-grained Factuality in Summarization
https://github.com/tagoyal/snac
공식 코드https://github.com/tahmedge/WSL-DS-COLING-2020
공식 코드https://github.com/tahmedge/preqfas
공식 코드Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization
https://github.com/takase/alone_seq2seq
공식 코드https://github.com/takase/control-length
공식 코드https://github.com/takase/rethink_perturbations
공식 코드Rethinking Perturbations in Encoder-Decoders for Fast Training
https://github.com/takatomokano/ted_summary
공식 코드Attention-based Multi-hypothesis Fusion for Speech Summarization
https://github.com/tanay2001/efactsum
공식 코드Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality
https://github.com/tanyuqian/aspect-based-summarization
공식 코드Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach
https://github.com/tanyuqian/ctc-gen-eval
공식 코드https://github.com/taokz/biomedgpt
공식 코드BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
https://github.com/teamreboott/mlbcap
공식 코드Multi-LLM Collaborative Caption Generation in Scientific Documents
https://github.com/temu-bsc/seq-to-seq-catalan
공식 코드https://github.com/tencent-ailab/grndpodcastsum
공식 코드Towards Abstractive Grounded Summarization of Podcast Transcripts
https://github.com/tencent-ailab/lodoss
공식 코드Toward Unifying Text Segmentation and Long Document Summarization
https://github.com/tencent-ailab/season
공식 코드Salience Allocation as Guidance for Abstractive Summarization
https://github.com/tensorflow/ranking
공식 코드TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
https://github.com/tensorflow/tensor2tensor
공식 코드https://github.com/testzer0/zs-summ-gpt3
공식 코드https://github.com/tgoldsack1/corpora_for_lay_summarisation
공식 코드Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
https://github.com/the-finai/pixiu
공식 코드FinBen: A Holistic Financial Benchmark for Large Language Models
https://github.com/theamrzaki/text_summurization_abstractive_methods
공식 코드https://github.com/thefonseca/factorsum
공식 코드Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents
https://github.com/thinkwee/DPP_CNN_Summarization
공식 코드https://github.com/tikhonovpavel/wikimulti
공식 코드https://github.com/timoschick/pet
공식 코드https://github.com/tjsiledar/summeval-op
공식 코드One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation
https://github.com/tohidarehman/hallucination-reduction-text-summarization
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.