├── prompts ├── std_generation_cnndm.txt ├── std_generation_xsum.txt ├── cot_generation_cnndm.txt ├── cot_generation_xsum.txt └── cot_element_extraction.txt ├── assets ├── Dataset.png └── Method.png ├── __pycache__ ├── arguments.cpython-38.pyc └── api_request.cpython-38.pyc ├── evaluation ├── __pycache__ │ ├── metric.cpython-36.pyc │ └── metric.cpython-38.pyc ├── metric.py └── eva.py ├── api_request.py ├── arguments.py ├── generation.py ├── README.md └── LICENSE /prompts/std_generation_cnndm.txt: -------------------------------------------------------------------------------- 1 | Summarize the above article: -------------------------------------------------------------------------------- /prompts/std_generation_xsum.txt: -------------------------------------------------------------------------------- 1 | Summarize the above article in one sentence: -------------------------------------------------------------------------------- /assets/Dataset.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Alsace08/SumCoT/HEAD/assets/Dataset.png -------------------------------------------------------------------------------- /assets/Method.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Alsace08/SumCoT/HEAD/assets/Method.png -------------------------------------------------------------------------------- /prompts/cot_generation_cnndm.txt: -------------------------------------------------------------------------------- 1 | Let's integrate the above information and summarize the article: -------------------------------------------------------------------------------- /prompts/cot_generation_xsum.txt: -------------------------------------------------------------------------------- 1 | Let's integrate the above information and summarize the article in one sentence: -------------------------------------------------------------------------------- /__pycache__/arguments.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Alsace08/SumCoT/HEAD/__pycache__/arguments.cpython-38.pyc -------------------------------------------------------------------------------- /__pycache__/api_request.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Alsace08/SumCoT/HEAD/__pycache__/api_request.cpython-38.pyc -------------------------------------------------------------------------------- /evaluation/__pycache__/metric.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Alsace08/SumCoT/HEAD/evaluation/__pycache__/metric.cpython-36.pyc -------------------------------------------------------------------------------- /evaluation/__pycache__/metric.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Alsace08/SumCoT/HEAD/evaluation/__pycache__/metric.cpython-38.pyc -------------------------------------------------------------------------------- /prompts/cot_element_extraction.txt: -------------------------------------------------------------------------------- 1 | What are the important entities in this document? 2 | What are the important dates in this document? 3 | What events are happening in this document? 4 | What is the result of these events? 5 | Please answer the above questions: -------------------------------------------------------------------------------- /api_request.py: -------------------------------------------------------------------------------- 1 | # -!- coding: utf-8 -!- 2 | import openai 3 | 4 | 5 | class Decoder: 6 | def __init__(self, api_key): 7 | self.api_key = api_key 8 | 9 | def decode(self, input, model, max_length): 10 | response = self.decoder_for_gpt3(model, input, max_length) 11 | return response 12 | 13 | def decoder_for_gpt3(self, model, input, max_length): 14 | openai.api_key = self.api_key 15 | 16 | if model == "gpt3": 17 | engine = "text-ada-001" 18 | elif model == "gpt3-medium": 19 | engine = "text-babbage-001" 20 | elif model == "gpt3-large": 21 | engine = "text-curie-001" 22 | elif model == "gpt3-xl": 23 | engine = "text-davinci-002" 24 | else: 25 | raise ValueError("model is not properly defined ...") 26 | 27 | response = openai.Completion.create( 28 | engine=engine, 29 | prompt=input, 30 | max_tokens=max_length, 31 | temperature=0, 32 | stop=None 33 | ) 34 | 35 | return response["choices"][0]["text"] -------------------------------------------------------------------------------- /evaluation/metric.py: -------------------------------------------------------------------------------- 1 | # -!- coding: utf-8 -!- 2 | import json 3 | import openai 4 | import argparse 5 | from rouge import Rouge 6 | from bert_score import score 7 | 8 | 9 | def rouge_score(ref, pred): 10 | rouge = Rouge() 11 | rs = rouge.get_scores(pred, ref) 12 | rouge1 = rs[0]["rouge-1"]["f"] * 100 13 | rouge2 = rs[0]["rouge-2"]["f"] * 100 14 | rougel = rs[0]["rouge-l"]["f"] * 100 15 | return rouge1, rouge2, rougel 16 | 17 | 18 | def bs_score(ref, pred): 19 | _, _, F1 = score([pred], [ref], lang="en", verbose=True) 20 | bs = F1.mean() 21 | return bs 22 | 23 | 24 | class BatchEvaluation: 25 | def __init__(self, total_r1=0, total_r2=0, total_rl=0, total_bs=0, 26 | call_time_rs=0, call_time_bs=0): 27 | self.ref = "" 28 | self.pred = "" 29 | 30 | self.total_r1 = total_r1 31 | self.total_r2 = total_r2 32 | self.total_rl = total_rl 33 | self.total_bs = total_bs 34 | self.call_time_rs = call_time_rs 35 | self.call_time_bs = call_time_bs 36 | 37 | def set_text(self, ref, pred): 38 | self.ref = ref 39 | self.pred = pred 40 | return self 41 | 42 | def get_rouge_score(self): 43 | r1, r2, rl = rouge_score(self.ref, self.pred) 44 | self.total_r1 += r1 45 | self.total_r2 += r2 46 | self.total_rl += rl 47 | self.call_time_rs += 1 48 | 49 | def get_bs_score(self): 50 | bs = bs_score(self.ref, self.pred) 51 | self.total_bs += bs 52 | self.call_time_bs += 1 53 | -------------------------------------------------------------------------------- /arguments.py: -------------------------------------------------------------------------------- 1 | # -!- coding: utf-8 -!- 2 | import argparse 3 | 4 | 5 | def get_prompt(): 6 | std_generation_cnndm_prompt = open("./prompts/std_generation_cnndm.txt").read() 7 | std_generation_xsum_prompt = open("./prompts/std_generation_xsum.txt").read() 8 | cot_generation_cnndm_prompt = open("./prompts/cot_generation_cnndm.txt").read() 9 | cot_generation_xsum_prompt = open("./prompts/cot_generation_xsum.txt").read() 10 | cot_extraction_prompt = "" 11 | for line in open("./prompts/cot_element_extraction.txt"): 12 | cot_extraction_prompt += line 13 | 14 | prompt = {"std_generation_cnndm_prompt": std_generation_cnndm_prompt, 15 | "std_generation_xsum_prompt": std_generation_xsum_prompt, 16 | "cot_generation_cnndm_prompt": cot_generation_cnndm_prompt, 17 | "cot_generation_xsum_prompt": cot_generation_xsum_prompt, 18 | "cot_extraction_prompt": cot_extraction_prompt} 19 | 20 | return prompt 21 | 22 | 23 | def parse_arguments(): 24 | parser = argparse.ArgumentParser(description="SumCoT") 25 | parser.add_argument("--cot_true", type=bool, default="False", 26 | help="standard or cot-based generation") 27 | parser.add_argument("--model", type=str, default="gpt3-xl", 28 | choices=["gpt3", "gpt3-medium", "gpt3-large", "gpt3-xl"], 29 | help="model used for decoding") 30 | parser.add_argument("--dataset", type=str, default="cnndm", 31 | choices=["cnndm", "xsum"], help="dataset source") 32 | parser.add_argument("--start_id", type=int, default="0") 33 | parser.add_argument("--end_id", type=int, default="0") 34 | args = parser.parse_args() 35 | 36 | prompt = get_prompt() 37 | args.cot = prompt["cot_extraction_prompt"] 38 | 39 | if args.dataset == "cnndm": 40 | args.std_prompt = prompt["std_generation_cnndm_prompt"] 41 | args.cot_prompt = prompt["cot_generation_cnndm_prompt"] 42 | elif args.dataset == "xsum": 43 | args.std_prompt = prompt["std_generation_xsum_prompt"] 44 | args.cot_prompt = prompt["cot_generation_xsum_prompt"] 45 | else: 46 | raise "Invalid Dataset!" 47 | 48 | return args 49 | -------------------------------------------------------------------------------- /generation.py: -------------------------------------------------------------------------------- 1 | # -!- coding: utf-8 -!- 2 | import json 3 | import os 4 | from api_request import Decoder 5 | from arguments import parse_arguments 6 | 7 | 8 | def get_llm_summary(args, decoder): 9 | in_file = os.path.join("./data", args.dataset+"_element_aware.json") 10 | with open(in_file, "r", encoding="utf-8") as f: 11 | if "cnndm" in in_file: 12 | data = json.load(f)["cnndm"] 13 | data_output = {"cnndm": []} 14 | elif "xsum" in in_file: 15 | data = json.load(f)["xsum"] 16 | data_output = {"xsum": []} 17 | else: 18 | raise "Invalid Dataset!" 19 | 20 | for i in range(args.start_id, args.end_id + 1): 21 | src = data[i]["src"] 22 | ori_sum = data[i]["original_summary"] 23 | new_sum = data[i]["element-aware_summary"] 24 | 25 | x = "Article: " + src + "\n" + args.std_prompt 26 | pred_std = decoder.decode(x, model=args.model, max_length=2048) 27 | 28 | x = "Article: " + src + "\n" + args.cot 29 | ele = decoder.decode(x, model=args.model, max_length=2048) 30 | x = x + ele + "\n" + args.cot_prompt 31 | pred_cot = decoder.decode(x, model=args.model, max_length=2048) 32 | 33 | if "cnndm" in in_file: 34 | data_output["cnndm"].append({"id": i, 35 | "src": src, 36 | "original_summary": ori_sum, 37 | "element-aware_summary": new_sum, 38 | "gpt3_summary": pred_std, 39 | "gpt3_cot_summary": pred_cot}) 40 | elif "xsum" in in_file: 41 | data_output["xsum"].append({"id": i, 42 | "src": src, 43 | "original_summary": ori_sum, 44 | "element-aware_summary": new_sum, 45 | "gpt3_summary": pred_std, 46 | "gpt3_cot_summary": pred_cot}) 47 | 48 | data_output = json.dumps(data_output, indent=2) 49 | if "cnndm" in in_file: 50 | with open("cnndm_output.json", "w", newline='\n') as g: 51 | g.write(data_output) 52 | if "xsum" in in_file: 53 | with open("xsum_output.json", "w", newline='\n') as g: 54 | g.write(data_output) 55 | 56 | 57 | if __name__ == '__main__': 58 | args = parse_arguments() 59 | decoder = Decoder(api_key="xxx") 60 | 61 | get_llm_summary(args, decoder) 62 | 63 | -------------------------------------------------------------------------------- /evaluation/eva.py: -------------------------------------------------------------------------------- 1 | # -!- coding: utf-8 -!- 2 | import json 3 | import os 4 | from metric import BatchEvaluation 5 | import argparse 6 | 7 | 8 | def batch_evalution(dataset, start_id, end_id, bs_true): 9 | in_file = os.path.join("../data", dataset+"_element_aware.json") 10 | with open(in_file, "r", encoding="utf-8") as f: 11 | if "cnndm" in in_file: 12 | data = json.load(f)["cnndm"] 13 | elif "xsum" in in_file: 14 | data = json.load(f)["xsum"] 15 | 16 | eva_ori_std = BatchEvaluation() # (original ref. summary) vs. (GPT-3 std. summary) 17 | eva_ori_cot = BatchEvaluation() # (original ref. summary) vs. (GPT-3 cot summary) 18 | eva_new_std = BatchEvaluation() # (element-aware ref. summary) vs. (GPT-3 std. summary) 19 | eva_new_cot = BatchEvaluation() # (element-aware ref. summary) vs. (GPT-3 cot summary) 20 | 21 | for i in range(start_id, end_id + 1): 22 | ori_ref = data[i]["original_summary"] 23 | new_ref = data[i]["element-aware_summary"] 24 | std_pred = data[i]["gpt3_summary"] 25 | cot_pred = data[i]["gpt3_cot_summary"] 26 | 27 | if ori_ref == "" or new_ref == "" or std_pred == "" or cot_pred == "": 28 | continue 29 | 30 | eva_ori_std.set_text(ori_ref, std_pred) 31 | eva_ori_std.get_rouge_score() 32 | if bs_true: eva_ori_std.get_bs_score() 33 | 34 | eva_ori_cot.set_text(ori_ref, cot_pred) 35 | eva_ori_cot.get_rouge_score() 36 | if bs_true: eva_ori_cot.get_bs_score() 37 | 38 | eva_new_std.set_text(new_ref, std_pred) 39 | eva_new_std.get_rouge_score() 40 | if bs_true: eva_new_std.get_bs_score() 41 | 42 | eva_new_cot.set_text(new_ref, cot_pred) 43 | eva_new_cot.get_rouge_score() 44 | if bs_true: eva_new_cot.get_bs_score() 45 | 46 | print(f"original ref. summary vs. GPT-3 std. summary:\n" 47 | f"batch size:{eva_ori_std.call_time_rs}\n" 48 | f"r1: {eva_ori_std.total_r1/eva_ori_std.call_time_rs}\n" 49 | f"r2: {eva_ori_std.total_r2/eva_ori_std.call_time_rs}\n" 50 | f"rl: {eva_ori_std.total_rl/eva_ori_std.call_time_rs}\n") 51 | 52 | #print(f"original ref. summary vs. GPT-3 cot summary:\n" 53 | #f"batch size:{eva_ori_cot.call_time_rs}\n" 54 | #f"r1: {eva_ori_cot.total_r1 / eva_ori_cot.call_time_rs}\n" 55 | #f"r2: {eva_ori_cot.total_r2 / eva_ori_cot.call_time_rs}\n" 56 | #f"rl: {eva_ori_cot.total_rl / eva_ori_cot.call_time_rs}\n") 57 | 58 | print(f"element-aware ref. summary vs. GPT-3 std. summary:\n" 59 | f"batch size:{eva_new_std.call_time_rs}\n" 60 | f"r1: {eva_new_std.total_r1 / eva_new_std.call_time_rs}\n" 61 | f"r2: {eva_new_std.total_r2 / eva_new_std.call_time_rs}\n" 62 | f"rl: {eva_new_std.total_rl / eva_new_std.call_time_rs}\n") 63 | 64 | print(f"element-aware ref. summary vs. GPT-3 cot summary:\n" 65 | f"batch size:{eva_new_std.call_time_rs}\n" 66 | f"r1: {eva_new_cot.total_r1 / eva_new_cot.call_time_rs}\n" 67 | f"r2: {eva_new_cot.total_r2 / eva_new_cot.call_time_rs}\n" 68 | f"rl: {eva_new_cot.total_rl / eva_new_cot.call_time_rs}\n") 69 | 70 | 71 | if __name__ == '__main__': 72 | parser = argparse.ArgumentParser(description="Evaluation") 73 | parser.add_argument("--dataset", type=str, default="cnndm", 74 | choices=["cnndm", "xsum"], help="dataset source") 75 | parser.add_argument("--start_id", type=int, default="0") 76 | parser.add_argument("--end_id", type=int, default="199") 77 | parser.add_argument("--bs_true", type=bool, default=False) 78 | args = parser.parse_args() 79 | #args.end_id = args.start_id 80 | batch_evalution(dataset=args.dataset, start_id=args.start_id, end_id=args.end_id, bs_true=args.bs_true) 81 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method (ACL'23 Long Paper) 2 | ![ACL 2023](https://img.shields.io/badge/ACL-2023-red.svg?style=plastic) 3 | ![python 3.6](https://img.shields.io/badge/python-3.6-royalblue.svg?style=plastic) 4 | ![license Apache-2.0](https://img.shields.io/badge/license-Apache%202.0-inactive.svg?style=plastic) 5 | 6 | 7 | 8 | 9 | **Let's elicit LLMs summarize step by step following the professional communication theory!** 10 | 11 | In this work, you can use directly or get inspired by: 12 | 13 | - A fine-grained generic summary data annotation protocol (combining micro and macro demands) 14 | 15 | - An expert-aligned generic summary test set (rewrite [*CNN/DailyMail*](https://paperswithcode.com/dataset/cnn-daily-mail-1) and [*BBC XSum*](https://paperswithcode.com/dataset/xsum)) 16 | 17 | - An expandable CoT-based open-end generation path (not only *SumCoT*) 18 | 19 | 20 | 21 | --- 22 | 23 | 24 | ## Element-aware Dataset 25 | 26 | ### Annotation Statement 27 | 28 | Our annotation protocol is mainly based on [*Lasswell Communication Model*](https://en.wikipedia.org/wiki/Lasswell%27s_model_of_communication) --- a famous communication theory proposed by Lasswell(1948). Additionally, we removed as much noise as possible from the original data set and performed data analysis (See paper for more details). 29 | 30 | Case comparisons for our Element-aware summary and original dataset-specific summary: 31 | 32 |
33 | 34 |
35 | 36 | ### Data Usage 37 | 38 | - See our constructed datasets in ```/data```. The two ```json``` files hold the data of *CNN/DailyMail* and *BBC/XSum*, respectively. 39 | 40 | - Each ```json``` file contains 200 samples, each containing the **source document**, **original summary**, **element-aware summary**, **GPT-3 summary (standard)**, and **GPT-3 summary (under SumCoT)**. You can use these data for more fine-grained evaluation. 41 | 42 | Dataset format: 43 | 44 | ```json 45 | { 46 | "dataset_name": [ 47 | { 48 | "id": 0, 49 | "src": "xxx", 50 | "original_summary": "xxx", 51 | "element-aware_summary": "xxx", 52 | "gpt3_summary": "xxx", 53 | "gpt3_cot_summary": "xxx" 54 | }, 55 | { 56 | "id": 1, 57 | "src": "xxx", 58 | "original_summary": "xxx", 59 | "element-aware_summary": "xxx", 60 | "gpt3_summary": "xxx", 61 | "gpt3_cot_summary": "xxx" 62 | }, 63 | ... 64 | { 65 | "id": 199, 66 | "src": "xxx", 67 | "original_summary": "xxx", 68 | "element-aware_summary": "xxx", 69 | "gpt3_summary": "xxx", 70 | "gpt3_cot_summary": "xxx" 71 | } 72 | ] 73 | } 74 | ``` 75 | 76 | 77 | ## Summary Chain-of-Thought (SumCoT) 78 | 79 | ### Pipeline 80 | 81 |
82 | 83 |
84 | 85 | ### Code Usage 86 | 87 | #### generation 88 | If you want to generate summaries (w/ or w/o SumCoT) by GPT-3, run ```generation.py```: 89 | 90 | ```shell 91 | python generation.py \ 92 | --dataset cnndm 93 | --start_id 0 94 | --end_id 199 95 | ``` 96 | 97 | where ```cot_true``` indicates if you want to use the SumCoT technique for generation, ```start_id``` and ```end_id``` determine the range of source documents in the dataset that you want to request. If you only want to generate the summary of the $a$-th sample, set ```start_id=end_id=a```. 98 | 99 | To facilitate the subsequent evaluation, the summaries just generated will be stored into ```$dataset_name$_output.json```. 100 | 101 | *Note: Don't forget to modify your ```openai_key``` in the Decoder().* 102 | 103 | #### evaluation 104 | 105 | 106 | We also provide code in ```/evaluation/eva.py``` to reproduce the results: 107 | 108 | ```shell 109 | python eva.py \ 110 | --dataset cnndm \ 111 | --start_id 0 \ 112 | --end_id 199 \ 113 | --bs_true false 114 | ``` 115 | 116 | where ```bs_true``` indicates if you want to use the bert score, ```start_id``` and ```end_id``` are the same as above. 117 | 118 | You can also build your own dataset ```json``` file, adjust ```start_id``` and ```end_id``` to perform your own evaluation. 119 | 120 | ## Reproducibility 121 | 122 | Experimental results in this paper are obtained during ***October-December 2022*** via the OpenAI API interface (basically before the ChatGPT release). If your reproduction results are slightly different from the results of the paper, you can report any of them (just indicate). 123 | 124 | ## Citation 125 | 126 | ``` 127 | @inproceedings{wang-etal-2023-element, 128 | title = "Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method", 129 | author = "Wang, Yiming and 130 | Zhang, Zhuosheng and 131 | Wang, Rui", 132 | booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", 133 | month = jul, 134 | year = "2023", 135 | address = "Toronto, Canada", 136 | publisher = "Association for Computational Linguistics", 137 | url = "https://aclanthology.org/2023.acl-long.482", 138 | doi = "10.18653/v1/2023.acl-long.482", 139 | pages = "8640--8665", 140 | } 141 | ``` -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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