File size: 6,316 Bytes
c7a6fe6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | def inference_prompt_revise_summary(fulltext, ref_summary, generated_summary, version, missing_subclaims):
prompt = f"""
You are a medical summarization model specialized in readability-controlled text revision.
Your task is to improve the **Generated Summary** by adding back the key missing clinical information listed under **Missing Subclaims**, while keeping the readability style defined for the level **{version}**.
Do not copy the reference summary. Keep coherence, brevity, and correctness.
---
### INPUT
**Full Text (for context):**
{fulltext}
**Reference Summary (for comparison only):**
{ref_summary}
**Generated Summary (to revise):**
{generated_summary}
**Missing Subclaims (to integrate naturally):**
{missing_subclaims}
---
### READABILITY STYLES
- **easy (FH 70–100, grade 5–7):**
- Short sentences, familiar vocabulary, concrete ideas.
- Avoid subordinate clauses and medical jargon.
- Tone: explanatory, simple, and friendly.
- **intermediate (FH 50–69, grade 8–12):**
- Moderate sentence complexity and domain vocabulary.
- Clear and structured explanation.
- **hard (FH 0–49, university/professional):**
- Use specialized terminology, formal and dense phrasing.
- Include:
- precise domain vocabulary;
- causal or analytical connectors (por consiguiente, sin embargo, dado que…);
- one definition, one process description, and one implication statement if possible;
- optional subordinate clauses for academic rhythm.
---
### OUTPUT
Return the result in the following JSON format:
{{
"revised_summary": "<your revised summary text here>"
}}
Ensure the text is coherent, medically accurate, and matches the **{version}** readability level.
"""
return prompt
from openai import OpenAI
import json
file_path = "/home/mshahidul/api_new.json"
with open(file_path, "r") as file:
api_keys = json.load(file)
openai_api_key = api_keys.get("openai")
client = OpenAI(api_key=openai_api_key)
def openai_return(prompt):
response = client.chat.completions.create(
model="gpt-5",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
)
try:
cleaned_response = response.choices[0].message.content.strip().replace("```json", "").replace("```", "")
return json.loads(cleaned_response)
except Exception as e:
return response.choices[0].message.content.strip().replace("```json", "").replace("```", "")
import json
file_path = "/home/mshahidul/readctrl/data/training_data_subclaim_verifier/synthetic_data_es_subclaims_100.json"
with open(file_path, 'r') as f:
synthetic_data = json.load(f)
with open("/home/mshahidul/readctrl/results/dataset_quality_check/completeness_resonability_check_100_qwen3-32B_v3.json", 'r') as f:
readability_reasoning = json.load(f)
import json, ast
reason_info = {}
for item in readability_reasoning:
id = item['id']
difficulty_level = item['version']
data_temp = item['completeness']
for _data in data_temp['results']:
reasonableness = _data['reasonableness']
# Step 1: Try to parse as JSON
if isinstance(reasonableness, str):
parsed = None
try:
parsed = json.loads(reasonableness)
except Exception:
try:
parsed = ast.literal_eval(reasonableness)
except Exception:
# Not JSON or dict — treat as plain text
parsed = {"reasonableness": "unknown", "justification": reasonableness}
reasonableness = parsed
# Step 2: Skip if "reasonable"
if reasonableness.get('reasonableness') in ["reasonable","unknown"]:
continue
# Step 3: Collect non-reasonable subclaims
key = (id, difficulty_level)
reason_info.setdefault(key, []).append(_data['subclaim'])
file_path_qwen3_32B = "/home/mshahidul/readctrl/results/dataset_quality_check/subclaim_verifier_results_100_qwen3-32B.json"
with open(file_path_qwen3_32B, 'r') as f:
qwen3_32B_results = json.load(f)
# def inference_prompt_revise_summary(fulltext, ref_summary, generated_summary, version, missing_subclaims):
import os
with open("/home/mshahidul/readctrl/data/testing_data_gs/multiclinsum_gs_train_es.json", "r") as f_train:
multiclinsum_gs_train_es = json.load(f_train)
dat_full_text={}
dat_summary={}
for item in multiclinsum_gs_train_es:
dat_full_text[item['id']]=item['fulltext']
dat_summary[item['id']]=item['summary']
res=[]
save_path = "/home/mshahidul/readctrl/results/dataset_quality_check/results_revised_100_gpt5_v3.json"
if os.path.exists(save_path):
with open(save_path, 'r') as f:
res = json.load(f)
existing_check=set((entry['id'], entry['difficulty_level']) for entry in res)
print(f"Resuming from {len(res)} entries")
import tqdm
for ind in tqdm.tqdm(range(0,10)):
for version in ["easy", "intermediate", "hard"]:
reference_summary = (f"{synthetic_data[ind]['ref_summary']['text']}")
generated_summary = (f"{synthetic_data[ind]['readability_versions'][version]['text']}")
if (synthetic_data[ind]['id'],version) in existing_check:
continue
if (synthetic_data[ind]['id'],version) not in reason_info or len(reason_info[(synthetic_data[ind]['id'],version)])==0:
continue
missing_subclaims = reason_info[(synthetic_data[ind]['id'],version)]
prompt = inference_prompt_revise_summary(dat_full_text[synthetic_data[ind]['id']], reference_summary, generated_summary, version, missing_subclaims)
try:
ans=openai_return(prompt)
res.append({
"id": synthetic_data[ind]['id'],
"difficulty_level": version,
"prompt": prompt,
"response": ans
})
if len(res)%2==0:
print(f"Completed {len(res)} out of 300")
with open(save_path, 'w') as outfile:
json.dump(res, outfile, indent=2)
except Exception as e:
print(f"Error at index {ind}, version {version}: {e}")
with open(save_path, 'w') as outfile:
json.dump(res, outfile, indent=2)
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