Paper Citation Record · LEDGER

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning

As of 17 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 2 inbound Pith citation observations for arXiv:2501.15398.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.15398 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:23:01.914337Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:33:03.169587Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-04T22:40:49.074122Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy22
unresolved5
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 01bd2883-3af0-4158-9297-b3b54efe3732 · outbound

This paper cites Green artificial intelligence initiatives: Potentials and challenges.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Green artificial intelligence initiatives: Potentials and challenges

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.177206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 009ff218-ddbb-4e03-ada1-7ee19c0a748c · outbound

This paper cites SciBERT: A pretrained language model for scientific text.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning SciBERT: A pretrained language model for scientific text

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.168248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b38de7f-6dd7-4aea-9d68-c3a8e7574aad · outbound

This paper cites Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.159411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f5b4c8fe-4b34-41f9-934f-6f387dbfd8fc · outbound

This paper cites How to estimate carbon footprint when training deep learning models? a guide and review.Environmental Research Communications, 5(11):115014, 2023.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning How to estimate carbon footprint when training deep learning models? a guide and review.Environmental Research Communications, 5(11):115014, 2023

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.150411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 696e1dc4-329c-43ee-8242-a0354e9b3597 · outbound

This paper cites Extracting highlights of scientific articles: A supervised summarization approach.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Extracting highlights of scientific articles: A supervised summarization approach

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.141260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d51a4a54-103a-42e2-b93d-a778b94aa4a1 · outbound

This paper cites A supervised approach to extractive summarisation of scientific papers.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning A supervised approach to extractive summarisation of scientific papers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.132192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6ba27062-be4f-467e-94f6-ac0ba1a51eb2 · outbound

This paper cites Efficiency – data centers, 2025.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Efficiency – data centers, 2025

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.122297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6f770ca7-24ca-4573-91da-ffd442aaa378 · outbound

This paper cites LORA: Low-rank adaptation of large language models.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning LORA: Low-rank adaptation of large language models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.113617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f1acde2c-8c40-4a16-98c4-4a375a745489 · outbound

This paper cites Global energy & CO 2 status report 2019, 2019.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Global energy & CO 2 status report 2019, 2019

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.104776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9c5c2dd7-5500-4fcd-ba7e-17f961741674 · outbound

This paper cites Green algorithms: quantifying the carbon footprint of computation.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Green algorithms: quantifying the carbon footprint of computation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.095613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7938ceb0-1ab7-4eb5-ba41-8ef7d35bf13e · outbound

This paper cites BART: Denoising sequence-to- sequence pre-training for natural language generation, translation, and comprehension.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning BART: Denoising sequence-to- sequence pre-training for natural language generation, translation, and comprehension

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.086626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b0a29030-cf25-433c-98c2-b7921f66d4a6 · outbound

This paper cites The automatic creation of literature abstracts.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning The automatic creation of literature abstracts

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.078284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad883a3f-52f2-4d05-9a6c-8828db796270 · outbound

This paper cites Large Language Models: A Survey.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Large Language Models: A Survey

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:23:01.876069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63d965a0-2ec7-4a0a-887e-960f0f3d4446 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Carbon Emissions and Large Neural Network Training

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:23:01.879646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa8b9477-f9e0-4a32-aa5d-e013ccac174b · outbound

This paper cites an unresolved cited work.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:23:02.070094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a2e1eb5f-1a04-4e2d-88fd-4951e7cf3e43 · outbound

This paper cites An analysis of abstractive text summarization using pre-trained models.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning An analysis of abstractive text summarization using pre-trained models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.062236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fadb5c2d-a9fa-492b-be7b-8027fcee3d38 · outbound

This paper cites Research highlight generation with ELMo contextual embeddings.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Research highlight generation with ELMo contextual embeddings

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.054062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c69dcdd2-1210-4dc6-8aa9-23ce5a4786cc · outbound

This paper cites Can pre-trained language models generate titles for research papers? In Proceedings of the International Conference on Asian Digital Libraries, pages 154–170.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Can pre-trained language models generate titles for research papers? In Proceedings of the International Conference on Asian Digital Libraries, pages 154–170

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.045242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dbe5ea64-f419-42b7-ad94-b64520bf66b5 · outbound

This paper cites Automatic generation of research highlights from scientific abstracts.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Automatic generation of research highlights from scientific abstracts

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.035778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3554318f-2d53-4ad1-85c3-cc1769ad4c41 · outbound

This paper cites Generation of highlights from research papers using pointer-generator networks and SciBERT embeddings.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Generation of highlights from research papers using pointer-generator networks and SciBERT embeddings

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.025408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1c04a35b-5323-4639-bda1-76f836080519 · outbound

This paper cites Named entity recognition based automatic generation of research highlights.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Named entity recognition based automatic generation of research highlights

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.015399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4e3ada36-5a48-401b-8cc2-f01cb91ef873 · outbound

This paper cites Green AI.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Green AI

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:02.005531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d871819-0a3d-4ad2-b561-89aa3ef5d176 · outbound

This paper cites See, Peter J.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning See, Peter J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:01.995744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8eda6388-c52d-4ea8-a0e9-b5176dbda573 · outbound

This paper cites Energy and policy considerations for deep learning in NLP.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Energy and policy considerations for deep learning in NLP

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:01.986041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a021e88e-4b35-4f1b-9907-8358cadbdd87 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning LLaMA: Open and Efficient Foundation Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T14:23:01.908116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e506ace-cb67-4983-ab7d-888d175db78e · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T14:23:01.911238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f61dc1ca-8b93-48ee-a7a6-af86de8d44d3 · outbound

This paper cites A systematic review of green AI.

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning A systematic review of green AI

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:23:01.968770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation df797af5-d785-4bd9-b090-12869ec2afb6 · inbound

Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects cites this paper.

Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:40:49.079614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0b25c5bf-e63e-4d13-99e5-99f46429879c · inbound

F$^2$Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading cites this paper.

F$^2$Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-08T04:33:03.169587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.