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d1221ff 999c3ec d1221ff 999c3ec d1221ff 999c3ec d1221ff 999c3ec d1221ff 999c3ec d1221ff | 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 | """Abstract base class for summarization tasks."""
from abc import ABC, abstractmethod
from typing import Dict, Any, Optional
class BaseSummarizationTask(ABC):
"""Base class for all summarization tasks.
Each task loads a dataset, samples an example, and constructs
the episode data (truncated context, question, ground truth answers).
"""
name: str = "base"
max_steps: int = 2 # default: summarize + answer
def infer_category(self, question: str) -> str:
"""Infer a coarse document category for richer benchmark metadata."""
q = question.lower()
if any(word in q for word in ["who", "born", "war", "empire", "king", "queen"]):
return "history"
if any(word in q for word in ["city", "country", "river", "mountain", "where"]):
return "geography"
if any(word in q for word in ["process", "chemical", "cell", "atom", "science"]):
return "science"
if any(word in q for word in ["programming", "software", "language", "python", "code"]):
return "software"
return "general"
@abstractmethod
def get_sample(self, seed: Optional[int] = None) -> Dict[str, Any]:
"""Return a single episode sample.
Returns a dict with:
- context: str Full context text
- truncated_context: str Visible portion of context
- truncation_ratio: float Fraction shown (e.g. 0.7)
- category: str Coarse domain/category
- source_type: str Source style (encyclopedic, report, paper, etc.)
- question: str The question to answer
- answer: str Primary ground-truth answer
- answer_list: list[str] All valid answers (for F1 scoring)
"""
def get_system_prompt(self) -> str:
return (
"You are preparing a compact working memory for another assistant that will "
"not get to read the original document. Summaries must be concise, faithful, "
"and dense with facts that are likely to matter for later question answering."
)
def get_summarize_prompt(self, truncated_context: str, truncation_ratio: float) -> str:
pct = int(truncation_ratio * 100)
return (
f"Here is a document excerpt (you are seeing approximately {pct}% of the full text):\n\n"
f"{truncated_context}\n\n"
"Write a compact summary for downstream use. Preserve concrete details such as "
"names, dates, quantities, entities, causal links, and definitions that a later "
"assistant might need in order to answer factual questions without the source text."
)
def get_answer_prompt(self, question: str) -> str:
return (
f"Based on your summary of the document, please answer the following question:\n\n"
f"Question: {question}\n\n"
"Answer using only information that was preserved in the summary. "
"Provide a direct, concise answer. If the answer is a specific name, number, or phrase, give just that."
)
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