feat: add extractive_summary workload and pipeline support

README.md

@@ -97,16 +97,17 @@ A minimal manifest lists ordered local node IDs and ordered jobs. Node entries m
     {"job_id": "text-stats-1", "job_type": "text_stats", "payload": {"text": "hello mesh\nhello node"}},
     {"job_id": "keyword-extract-1", "job_type": "keyword_extract", "payload": {"text": "AetherMesh nodes process useful local work for the mesh.", "limit": 5}},
     {"job_id": "text-chunk-1", "job_type": "text_chunk", "payload": {"text": "AetherMesh prepares local text chunks for future AI processing.", "max_chars": 24}},
-    {"job_id": "text-embed-1", "job_type": "text_embed", "payload": {"text": "AetherMesh nodes prepare deterministic local feature vectors.", "dimensions": 8}}
+    {"job_id": "text-embed-1", "job_type": "text_embed", "payload": {"text": "AetherMesh nodes prepare deterministic local feature vectors.", "dimensions": 8}},
+    {"job_id": "extractive-summary-1", "job_type": "extractive_summary", "payload": {"text": "AetherMesh nodes run local useful work. Local work reports deterministic outputs. Deterministic outputs make validation and contribution accounting auditable.", "max_sentences": 2}}
   ]
 }
 ```
 
-The simulation output includes per-result validation details and a compact `validation_summary`. Contribution credit is recorded only for validated completed `echo`, deterministic `text_stats`, deterministic `keyword_extract`, deterministic `text_chunk`, and deterministic `text_embed` text-preprocessing results. Invalid or unsupported results remain visible in the output for local audit/debugging and earn zero contribution units.
+The simulation output includes per-result validation details and a compact `validation_summary`. Contribution credit is recorded only for validated completed `echo`, deterministic `text_stats`, deterministic `keyword_extract`, deterministic `text_chunk`, deterministic `text_embed`, and deterministic `extractive_summary` text-preprocessing results. Invalid or unsupported results remain visible in the output for local audit/debugging and earn zero contribution units.
 
-Prototype contribution scoring is deterministic, integer-only, capped, and local-only. The current rules are intentionally simple: `echo` earns 1 unit; `text_stats` earns 1 base unit plus 1 unit per 100-character output bucket capped at 5; `keyword_extract` earns 1 base unit plus 1 unit per 5 extracted unique-keyword bucket capped at 5; `text_chunk` earns 1 base unit plus 1 unit per 2 emitted chunks capped at 5; and `text_embed` earns 1 base unit plus bounded token-count and vector-dimension bucket units capped at 6. These units are prototype accounting for local auditability only; they are not rewards, payments, reputation, tokenomics, payouts, or an economic fairness model.
+Prototype contribution scoring is deterministic, integer-only, capped, and local-only. The current rules are intentionally simple: `echo` earns 1 unit; `text_stats` earns 1 base unit plus 1 unit per 100-character output bucket capped at 5; `keyword_extract` earns 1 base unit plus 1 unit per 5 extracted unique-keyword bucket capped at 5; `text_chunk` earns 1 base unit plus 1 unit per 2 emitted chunks capped at 5; `text_embed` earns 1 base unit plus bounded token-count and vector-dimension bucket units capped at 6; and `extractive_summary` earns 1 base unit plus 1 unit per 2 selected-sentence bucket capped at 5. These units are prototype accounting for local auditability only; they are not rewards, payments, reputation, tokenomics, payouts, or an economic fairness model.
 
-The `text_chunk` workload accepts plain input text plus optional `max_chars` (default 120, range 1-1000), preserves input character order, prefers whitespace boundaries, and splits long continuous spans at the character limit. The `text_embed` workload is prototype-only deterministic local feature extraction, not a real semantic ML embedding model; it accepts non-empty text plus optional `dimensions` (default 8, range 2-64) and returns exact integer SHA-256 token-bucket counts such as `{"dimensions": 4, "token_count": 3, "unique_terms": 2, "vector": [0, 2, 1, 0]}`.
+The `text_chunk` workload accepts plain input text plus optional `max_chars` (default 120, range 1-1000), preserves input character order, prefers whitespace boundaries, and splits long continuous spans at the character limit. The `text_embed` workload is prototype-only deterministic local feature extraction, not a real semantic ML embedding model; it accepts non-empty text plus optional `dimensions` (default 8, range 2-64) and returns exact integer SHA-256 token-bucket counts such as `{"dimensions": 4, "token_count": 3, "unique_terms": 2, "vector": [0, 2, 1, 0]}`. The `extractive_summary` workload is deterministic local extractive summarization, not real semantic LLM summarization; it accepts non-empty text plus optional `max_sentences` (default 3, range 1-10), splits on simple punctuation/newline boundaries, scores sentences with local token-frequency heuristics, and returns the selected source sentences in original order.
 
 The local-only `node_roster` includes each node's `node_id`, `status`, assignment count, contribution units, and deterministic heartbeat metadata. `heartbeat_sequence` and `heartbeat_count` are in-memory simulation counters recorded when available nodes start a local run; they are not real network heartbeats or wall-clock liveness timestamps.
 

examples/local-batch.json

@@ -39,6 +39,14 @@
         "text": "AetherMesh nodes prepare deterministic local feature vectors.",
         "dimensions": 8
       }
+    },
+    {
+      "job_id": "extractive-summary-1",
+      "job_type": "extractive_summary",
+      "payload": {
+        "text": "AetherMesh nodes run local useful work. Local work reports deterministic outputs. Deterministic outputs make validation and contribution accounting auditable.",
+        "max_sentences": 2
+      }
     }
   ]
 }

graphify-out/.graphify_labels.json

@@ -20,13 +20,5 @@
   "18": "Community 18",
   "19": "Community 19",
   "20": "Community 20",
-  "21": "Community 21",
-  "22": "Community 22",
-  "23": "Community 23",
-  "24": "Community 24",
-  "25": "Community 25",
-  "26": "Community 26",
-  "27": "Community 27",
-  "28": "Community 28",
-  "29": "Community 29"
+  "24": "Community 24"
 }